{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# created by Zhaomiao Guo 03/23/2020\n",
    "\n",
    "# import traci related packages\n",
    "from __future__ import absolute_import\n",
    "from __future__ import print_function\n",
    "import os\n",
    "import sys\n",
    "import optparse\n",
    "import random\n",
    "import pandas as pd\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "# we need to import python modules from the $SUMO_HOME/tools directory\n",
    "if 'SUMO_HOME' in os.environ:\n",
    "    tools = os.path.join(os.environ['SUMO_HOME'], 'tools')\n",
    "    sys.path.append(tools)\n",
    "else:\n",
    "    sys.exit(\"please declare environment variable 'SUMO_HOME'\")\n",
    "\n",
    "from sumolib import checkBinary  # noqa\n",
    "from sumolib.net import readNet  # noqa\n",
    "import traci  # noqa\n",
    "\n",
    "# import learning related packages\n",
    "%matplotlib notebook\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as ani\n",
    "import matplotlib.cm as cm\n",
    "import pickle  # to save/load Q-Tables\n",
    "import time  # using this to keep track of our saved Q-Tables.\n",
    "from tqdm import notebook as tqdm\n",
    "from collections import deque\n",
    "from PIL import Image\n",
    "#import cv2\n",
    "\n",
    "from textwrap import wrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from scipy.optimize import least_squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"data_segments/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#traci.close([\"SUMO\", \"-c\", DATA_DIR+\"test.sumocfg\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Retrying in 1 seconds\n"
     ]
    }
   ],
   "source": [
    "class SUMOEnv:    \n",
    "    RETURN_IMAGES = False\n",
    "    ACCIDENT_START =0\n",
    "    ACCIDENT_END = 0 # there is no accident happens\n",
    "    ACCIDENT_EDGE_ID = \"e5.66\"\n",
    "    INFO_EDGE_ID = \"e1\"\n",
    "    episode_step = 0\n",
    "\n",
    "    max_speed = {\"e0\":140, \"e1\":40,\"e1.37\":40,\"e1.74\":40,\"e2\":40,\"e2.37\":40,\"e2.74\":40,\"e3\":40,\n",
    "                 \"e3.37\":40,\"e3.74\":40,\"e4\":40,\"e4.37\":40,\"e4.74\":40,\"e5\":40, \"e6\":40}\n",
    "    max_speed_accident = {(ACCIDENT_EDGE_ID, 1): 0.001, (ACCIDENT_EDGE_ID, 0): 100}\n",
    "    estimate_tt = {ACCIDENT_EDGE_ID: 20, 'e5.33': 20, 'e5':20}\n",
    "    \"\"\"max_speed = {\"e0\":20, \"e1\":20,\"e2\":5,\"e3\":5,\"e4\":20,\"e5\":20,\"e6\":20}\n",
    "    max_speed_accident = {(ACCIDENT_EDGE_ID, 1): 0.001, (ACCIDENT_EDGE_ID, 0): 20}\n",
    "    estimate_tt = {ACCIDENT_EDGE_ID: 1, \"e5\": 1, \"e5.33\": 1}\"\"\"\n",
    "    \n",
    "    # e3 with subedges length total 1000\n",
    "    def set_length():\n",
    "        mytreenet = ET.parse('data_segments/test.net.xml')\n",
    "        myrootnet = mytreenet.getroot()\n",
    "        #myrootnet[5][0].set('length', str(0))\n",
    "        #myrootnet[6][0].set('length', str(0))\n",
    "        myrootnet[26][0].set('length', str(100))\n",
    "        myrootnet[27][0].set('length', str(1609.34*1))\n",
    "        myrootnet[28][0].set('length', str(100))\n",
    "        mytreenet.write('data_segments/test.net.xml')\n",
    "    \n",
    "     # get edge information once at the beginning of the simulation. \n",
    "    def get_edge_info():\n",
    "        def get_edge_length(edge_id_list):\n",
    "            edge_length = {}\n",
    "            for edge_id in edge_id_list:\n",
    "                edge_length[edge_id] = traci.lane.getLength(edge_id + \"_0\")\n",
    "            return edge_length\n",
    "\n",
    "        def get_edge_lanes(edge_id_list):\n",
    "            edge_lanes = {}\n",
    "            for edge_id in edge_id_list:\n",
    "                edge_lanes[edge_id] = traci.edge.getLaneNumber(edge_id)\n",
    "            return edge_lanes\n",
    "        traci.start([\"sumo\", \"-c\", DATA_DIR+\"test.sumocfg\"])\n",
    "        # get the length of each edge, do not include the intersection connection edge\n",
    "        edge_id_list = list(traci.edge.getIDList())\n",
    "        edge_id_list = [x for x in edge_id_list if \":\" not in x] # remove all the intersections\n",
    "        edge_length = get_edge_length(edge_id_list) \n",
    "        edge_lanes = get_edge_lanes(edge_id_list)\n",
    "        traci.close([\"SUMO\", \"-c\", DATA_DIR+\"test.sumocfg\"])\n",
    "\n",
    "        return edge_id_list, edge_length, edge_lanes\n",
    "    \n",
    "    set_length()\n",
    "    edge_id_list, edge_length, edge_lanes = get_edge_info()\n",
    "\n",
    "    OBSERVATION_SPACE_VALUES = len(edge_id_list)+len(list([x for x in edge_id_list if \"e1\" in x])) # 3: 1. ACCIDENT? 2. time step. This is finite horizon  \n",
    "    ACTION_SPACE_SIZE = 2 # share and not share information\n",
    "    \n",
    "    # the dict! (colors) 1,2,3 are state index\n",
    "    d = {1: (255, 175, 0),\n",
    "         2: (0, 255, 0),\n",
    "         3: (0, 0, 255)}\n",
    "    \n",
    "    def set_max_speed(self, max_speed):\n",
    "        for edge_id in self.edge_id_list:\n",
    "            if (edge_id[0] != ':'): # not intersection\n",
    "                if (edge_id in max_speed.keys()):\n",
    "                    traci.edge.setMaxSpeed(edgeID=edge_id, speed=max_speed[edge_id])\n",
    "                else:\n",
    "                    traci.edge.setMaxSpeed(edgeID=edge_id, speed=max_speed[edge_id.split('.')[0]])\n",
    "\n",
    "    def update_max_speed(self, edge_id, accident):\n",
    "        # set the speed limit of edge e5\n",
    "        traci.edge.setMaxSpeed(edgeID=edge_id, speed=self.max_speed_accident[edge_id, accident])\n",
    "\n",
    "    def get_state(self, edge_id_list, edge_length, edge_lanes, action):\n",
    "        state = []\n",
    "        num_vehicle = {}\n",
    "        density = {}\n",
    "        speed = {}\n",
    "        flow = {}\n",
    "        for edge_id in edge_id_list:\n",
    "            num_vehicle[edge_id] = traci.edge.getLastStepVehicleNumber(edgeID=edge_id)\n",
    "            density[edge_id] = num_vehicle[edge_id] / edge_length[edge_id]/edge_lanes[edge_id]\n",
    "            #getLastStepVehicleIDs, if no vehicle, speed = max\n",
    "            speed[edge_id] = traci.edge.getLastStepMeanSpeed(edgeID = edge_id)\n",
    "            #state = state+[density[edge_id],speed[edge_id]]\n",
    "            flow[edge_id] = density[edge_id]*speed[edge_id]\n",
    "            state = state+[density[edge_id]]\n",
    "        for edge_id in edge_id_list:\n",
    "            if edge_id[0:2] == \"e1\":\n",
    "                lane_id = edge_id+\"_0\"\n",
    "                num_vehicle[lane_id] = traci.lane.getLastStepVehicleNumber(laneID=lane_id)\n",
    "                density[lane_id] = num_vehicle[lane_id] / edge_length[edge_id]\n",
    "                state = state + [density[lane_id]]\n",
    "        \n",
    "        \"\"\"veh_num = traci.inductionloop.getLastStepVehicleNumber(\"d0\") + traci.inductionloop.getLastStepVehicleNumber(\"d1\")\n",
    "        \n",
    "        if action == 1:\n",
    "            count_green = veh_num\n",
    "        else:\n",
    "            count_red = veh_num\n",
    "        \"\"\"\n",
    "        \"\"\"for veh_id in traci.inductionloop.getLastStepVehicleIDs(\"d0\") + traci.inductionloop.getLastStepVehicleIDs(\"d1\"):\n",
    "            if action == 1: # route according to real travel time\n",
    "                count_green +=1\n",
    "            else: # route according to estimate travel time\n",
    "                count_red +=1\"\"\"\n",
    "            \n",
    "        return state, flow\n",
    "\n",
    "    def is_accident(self):\n",
    "        if self.episode_step >= self.ACCIDENT_START and self.episode_step<self.ACCIDENT_END:\n",
    "            accident = 1\n",
    "        else:\n",
    "            accident = 0\n",
    "        \n",
    "        return accident\n",
    "\n",
    "    def get_tt_set_adapt_tt(self):\n",
    "        travel_time = {}\n",
    "    # current travel time for each edge\n",
    "        for edge_id in self.edge_id_list:\n",
    "            travel_time[edge_id] = traci.edge.getTraveltime(edge_id) # real travel time\n",
    "            traci.edge.adaptTraveltime(edgeID=edge_id, time=travel_time[edge_id]) # all the edges use the real travel time except for edge e5\n",
    "        \n",
    "        for edge_id in self.estimate_tt.keys():\n",
    "            traci.edge.adaptTraveltime(edgeID=edge_id, time=self.estimate_tt[edge_id])# people are uncertain about the travel time on e5 and assume it is 5 \n",
    "\n",
    "        return travel_time\n",
    "\n",
    "    def reroute_veh(self, action, travel_time, estimate_tt):\n",
    "        count_green = 0\n",
    "        count_red = 0\n",
    "        # when vehicle passes through e0\n",
    "        for veh_id in traci.inductionloop.getLastStepVehicleIDs(\"d3\") + traci.inductionloop.getLastStepVehicleIDs(\"d4\") + traci.inductionloop.getLastStepVehicleIDs(\"d2\"):\n",
    "            traci.vehicle.setColor(vehID=veh_id, color=(255,255,255))\n",
    "            #traci.vehicle.rerouteTraveltime(vehID=veh_id, currentTravelTimes=False)\n",
    "            #print(traci.vehicle.getAdaptedTravelTime(vehID=veh_id))\n",
    "\n",
    "        # when vehicle passes through e1\n",
    "        for veh_id in traci.inductionloop.getLastStepVehicleIDs(\"d0\") + traci.inductionloop.getLastStepVehicleIDs(\"d1\"):\n",
    "            if action == 1: # route according to real travel time\n",
    "                traci.vehicle.setAdaptedTraveltime(vehID=veh_id, edgeID=self.ACCIDENT_EDGE_ID, time=travel_time[self.ACCIDENT_EDGE_ID])\n",
    "                traci.vehicle.setColor(vehID=veh_id, color=(0,255,0))\n",
    "                count_green += 1\n",
    "            else: # route according to estimate travel time\n",
    "                traci.vehicle.setAdaptedTraveltime(vehID=veh_id, edgeID=self.ACCIDENT_EDGE_ID, time=estimate_tt[self.ACCIDENT_EDGE_ID])\n",
    "                traci.vehicle.setColor(vehID=veh_id, color=(255,0,0))\n",
    "                count_red += 1\n",
    "            traci.vehicle.rerouteTraveltime(vehID=veh_id, currentTravelTimes=False)\n",
    "            \n",
    "        return count_green, count_red\n",
    "\n",
    "    def calc_reward(self, flow):\n",
    "        tt = 0\n",
    "        reward  = 0\n",
    "        for edge_id in self.edge_id_list:\n",
    "            tt = tt + traci.edge.getLastStepVehicleNumber(edge_id) # total travel time as reward\n",
    "            #reward = reward + flow[edge_id] # flow as reward\n",
    "            reward = reward - traci.edge.getLastStepVehicleNumber(edge_id) # total travel time as reward\n",
    "        #reward = traci.simulation.getArrivedNumber()\n",
    "            \n",
    "        return tt, reward\n",
    "\n",
    "    def observe(self, action, count_green, count_red):\n",
    "        if self.RETURN_IMAGES:\n",
    "            observation = np.array(self.get_image())\n",
    "        else:\n",
    "            observation, flow = self.get_state(self.edge_id_list, self.edge_length, self.edge_lanes, action)\n",
    "            \n",
    "        accident = self.is_accident()\n",
    "        #observation = observation + [accident, self.episode_step]\n",
    "        return observation, accident, flow\n",
    "    \n",
    "    def reset(self, action):\n",
    "        self.episode_step = 0\n",
    "        traci.load(['-c', DATA_DIR+\"test.sumocfg\"])\n",
    "        self.set_max_speed(self.max_speed)\n",
    "        traci.simulationStep(40) # warm up: simulate0s to set up the vehicle locations and estimate speed\n",
    "        observation, accident, _ = self.observe(action, 0, 0)\n",
    "        \n",
    "        return observation, accident\n",
    "\n",
    "    def step(self, action):\n",
    "        self.episode_step += 1\n",
    "        travel_time = self.get_tt_set_adapt_tt() # get real travel time and set adapt travel time for routing purposes. note that edge e5 the real and adapt travel time are not equal due to uncertainties\n",
    "        #print(travel_time)\n",
    "        count_green, count_red = self.reroute_veh(action, travel_time, self.estimate_tt)                     \n",
    "        \n",
    "        traci.simulationStep() # simulate one step after decision is made to get new state\n",
    "        \n",
    "        new_observation, accident, flow = self.observe(action, count_green, count_red)\n",
    "        self.update_max_speed(self.ACCIDENT_EDGE_ID, accident) # update max speed for edge 5 based on if accident happens. Note that we need to update both accident and no accident case since it's an iteration\n",
    "        tt, reward = self.calc_reward(flow)\n",
    "        \n",
    "        done = False\n",
    "        if traci.simulation.getMinExpectedNumber() == 0 or self.episode_step > 10:\n",
    "            done = True\n",
    "                  \n",
    "        return new_observation, tt, reward, done, accident\n",
    "\n",
    "    def render(self):\n",
    "        img = self.get_image()\n",
    "        img = img.resize((300, 300))  # resizing so we can see our agent in all its glory.\n",
    "        cv2.imshow(\"image\", np.array(img))  # show it!\n",
    "        cv2.waitKey(1)\n",
    "\n",
    "    # FOR CNN #\n",
    "    def get_image(self):\n",
    "        pass\n",
    "    \n",
    "env = SUMOEnv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_od_flow(flow):\n",
    "    mytree = ET.parse('data_segments/test.rou.xml')\n",
    "    myroot = mytree.getroot()\n",
    "    myroot[5].set('vehsPerHour', str(flow))\n",
    "    mytree.write('data_segments/test.rou.xml')\n",
    "    \n",
    "def set_initial_vehicles(period,period2):\n",
    "    mytree = ET.parse('data_segments/test.rou.xml')\n",
    "    myroot = mytree.getroot()\n",
    "    myroot[4].set('period', str(period))\n",
    "    myroot[3].set('period', str(period2))\n",
    "    mytree.write('data_segments/test.rou.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Retrying in 1 seconds\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7feda46d29dc41dc9593b296b62430c2",
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       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# global parameters\n",
    "EPSILON = 0.1\n",
    "\n",
    "# Environment \"settings\n",
    "EPISODES = 100\n",
    "\n",
    "# For stats\n",
    "ep_rewards = []\n",
    "ep_tts = []\n",
    "\n",
    "# For more repetitive results\n",
    "random.seed(3)\n",
    "np.random.seed(4)\n",
    "\n",
    "# start sumo first \n",
    "traci.start([\"sumo\", \"-c\", DATA_DIR+\"test.sumocfg\"])\n",
    "\n",
    "TTC_5 = {}\n",
    "CollisionRisk = {}\n",
    "flow = {}\n",
    "speed = {}\n",
    "speed_mean={}\n",
    "density = {}\n",
    "num_vehicle = {}\n",
    "veh = {}\n",
    "ttc = {}\n",
    "risk = {}\n",
    "count = {}\n",
    "TTC_episodes=[]\n",
    "FLOW_episodes=[]\n",
    "DENSITY_episodes=[]\n",
    "SPEED_episodes=[]\n",
    "MAX_DENSITY = 0.3\n",
    "MAX_FLOW = 5000\n",
    "MAX_SPEED = 14\n",
    "odflow = {}\n",
    "INITIAL_TIME_ADD_VEHICLES = 10\n",
    "TTC_THRESHOLD = 3.5\n",
    "Link_tt = {}\n",
    "\n",
    "\n",
    "# Iterate over episodes\n",
    "for episode in tqdm.tqdm(range(1, EPISODES + 1), ascii=True, unit='episodes'):\n",
    "    od_flow = episode/EPISODES * MAX_FLOW \n",
    "    period = INITIAL_TIME_ADD_VEHICLES/(episode/EPISODES * MAX_DENSITY * (env.edge_length[\"e3.37\"]))\n",
    "    period2 = INITIAL_TIME_ADD_VEHICLES/(episode/EPISODES * MAX_DENSITY * (env.edge_length[\"e3.74\"]))\n",
    "    \n",
    "    \n",
    "    #print(period)\n",
    "    set_od_flow(od_flow)\n",
    "    set_initial_vehicles(period,period2)\n",
    "    \n",
    "    state_history = {}\n",
    "    accident_history = {}\n",
    "    action_history = {}\n",
    "    reward_history = {}\n",
    "\n",
    "    # Restarting episode - reset episode reward and step number\n",
    "    episode_reward = 0\n",
    "    episode_tt = 0\n",
    "    step = 0\n",
    "    action = 0\n",
    "    # Reset environment and get initial state\n",
    "    current_state, accident = env.reset(action) # current state are all the density and speed of each edge (including intersection?)\n",
    "    \n",
    "    if EPISODES - episode < 10000:\n",
    "        traci.edge.setMaxSpeed(edgeID=\"e3.37\", speed=traci.edge.getLastStepMeanSpeed(edgeID = \"e3.37\"))\n",
    "    state_history[step] = current_state\n",
    "    accident_history[step] = accident\n",
    "    # Reset flag and start iterating until episode ends\n",
    "    done = False\n",
    "    while (not done):\n",
    "\n",
    "        state_new, tt, reward, done, accident_new = env.step(action)\n",
    "        \n",
    "  \n",
    "        for edge_id in env.edge_id_list:\n",
    "            odflow[episode, step,edge_id] = od_flow\n",
    "            #print(\"step2\",step2)\n",
    "            num_vehicle[step,edge_id] = traci.edge.getLastStepVehicleNumber(edgeID=edge_id)\n",
    "            density[episode, step,edge_id] = num_vehicle[step, edge_id] / env.edge_length[edge_id]/env.edge_lanes[edge_id]\n",
    "            #print('density',density)\n",
    "            veh[step,edge_id] = traci.edge.getLastStepVehicleIDs(edgeID=edge_id)\n",
    "            \n",
    "            # calculate space mean speed\n",
    "            temp = 0\n",
    "            temp1 = 0\n",
    "            for v in veh[step,edge_id]:\n",
    "                if traci.vehicle.getSpeed(v) > 0:\n",
    "                    if traci.vehicle.getSpeed(v) > 0:\n",
    "                        temp += 1/traci.vehicle.getSpeed(v)\n",
    "                    else:\n",
    "                        temp+= 1/0.0001\n",
    "                    temp1 += traci.vehicle.getSpeed(v)\n",
    "                    \n",
    "            if (temp !=0) & (temp1 != 0): # temp == 0 when no vehicles on this link; temp1 == 0 when zero speed. both should have flow equals to zero\n",
    "                space_mean_speed = len(veh[step,edge_id])/temp\n",
    "                time_mean_speed = temp1/len(veh[step,edge_id]) # same as speed[step,edge_id] = traci.edge.getLastStepMeanSpeed(edgeID = edge_id)\n",
    "                speed[episode, step,edge_id] = space_mean_speed\n",
    "                speed_mean[episode, step,edge_id] = time_mean_speed\n",
    "            else:\n",
    "                speed[episode, step,edge_id] = 0 # \n",
    "                speed_mean[episode, step,edge_id] =0\n",
    "            \n",
    "            flow[episode, step,edge_id] = density[episode, step,edge_id] * speed[episode, step,edge_id] * 3600 # convert unit to veh/hour\n",
    "            if speed[episode, step,edge_id] > 0:\n",
    "                Link_tt[episode, step,edge_id]= env.edge_length[edge_id]/ speed[episode, step,edge_id]\n",
    "            else:\n",
    "                Link_tt[episode, step,edge_id]= 0\n",
    "            # calculate ttc\n",
    "            \n",
    "            ttc[step,edge_id] = []\n",
    "            risk[step,edge_id] = []\n",
    "            count[step,edge_id] = 0\n",
    "            for v in veh[step,edge_id]:\n",
    "                leader = traci.vehicle.getLeader(vehID = v,dist = 0) # just focus on its current lane\n",
    "                if leader != None:\n",
    "                    lead_speed = traci.vehicle.getSpeed(vehID = leader[0])\n",
    "                    follow_speed = traci.vehicle.getSpeed(vehID = v)\n",
    "                    speed_diff = (follow_speed - lead_speed)\n",
    "                    space_gap = leader[1]+traci.vehicle.getMinGap(vehID = v) # leader has two elements: [0] vehID; [1] distance - minGap\n",
    "                    if speed_diff >= EPSILON:\n",
    "                        ttc[step,edge_id].append(space_gap/speed_diff)\n",
    "                        risk[step,edge_id].append(speed_diff/space_gap)\n",
    "                        #print('ttc', edge_id, ttc[step,edge_id])\n",
    "                        count[step,edge_id] += 1\n",
    "                    \n",
    "            if (count[step,edge_id] > 1):\n",
    "                TTC_5[episode, step,edge_id] = np.percentile(np.array(ttc[step,edge_id]),0.05)\n",
    "                CollisionRisk[episode, step,edge_id] = np.percentile(np.array(risk[step,edge_id]),0.05)\n",
    "                #CollisionRisk[episode,step,edge_id] = min(risk[step,edge_id])\n",
    "                #TTC_5[step,edge_id] = min(ttc[step,edge_id])\n",
    "                #print('CollisionRisk', edge_id, risk[step,edge_id])\n",
    "            else:\n",
    "                TTC_5[episode, step,edge_id] = np.nan\n",
    "                CollisionRisk[episode, step,edge_id] = np.nan\n",
    "                    \n",
    "        # store state and reward\n",
    "        action_history[step] = action\n",
    "        state_history[step+1] = state_new\n",
    "        accident_history[step+1] = accident_new\n",
    "        reward_history[step+1] = reward\n",
    "        step += 1\n",
    "        \n",
    "traci.close([\"SUMO\", \"-c\", DATA_DIR+\"test.sumocfg\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#traci.close([\"SUMO\", \"-c\", DATA_DIR+\"test.sumocfg\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"episode\",\"step\", \"edge_id\"]\n",
    "df1 = pd.DataFrame(list(flow.keys()), columns = column_names)\n",
    "df1['Time_to_collision'] = df1.set_index(column_names).index.map(TTC_5.get)\n",
    "df1['Space_mean_speed'] = df1.set_index(column_names).index.map(speed.get)\n",
    "df1['Density'] = df1.set_index(column_names).index.map(density.get)\n",
    "df1['Flow'] = df1.set_index(column_names).index.map(flow.get)\n",
    "df1['Od_flow'] = df1.set_index(column_names).index.map(odflow.get)\n",
    "df1['CollisionRisk'] = df1.set_index(column_names).index.map(CollisionRisk.get)\n",
    "#df1['CollisionRisk'] = df1.set_index(column_names).index.map(CollisionRisk.get)\n",
    "df1[\"Travel_time\"] = df1.set_index(column_names).index.map(Link_tt.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "episode              False\n",
       "step                 False\n",
       "edge_id              False\n",
       "Time_to_collision    False\n",
       "Space_mean_speed     False\n",
       "Density              False\n",
       "Flow                 False\n",
       "Od_flow              False\n",
       "CollisionRisk        False\n",
       "Travel_time          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df1 = df1[df1['edge_id']== 'e3.37']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>episode</th>\n",
       "      <th>step</th>\n",
       "      <th>edge_id</th>\n",
       "      <th>Time_to_collision</th>\n",
       "      <th>Space_mean_speed</th>\n",
       "      <th>Density</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Od_flow</th>\n",
       "      <th>CollisionRisk</th>\n",
       "      <th>Travel_time</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>18695</th>\n",
       "      <td>100</td>\n",
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       "      <td>e4.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>18696</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
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       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>18700 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       episode  step edge_id  Time_to_collision  Space_mean_speed  Density  \\\n",
       "0            1     0      e0                NaN               0.0      0.0   \n",
       "1            1     0      e1                NaN               0.0      0.0   \n",
       "2            1     0   e1.37                NaN               0.0      0.0   \n",
       "3            1     0   e1.74                NaN               0.0      0.0   \n",
       "4            1     0      e2                NaN               0.0      0.0   \n",
       "...        ...   ...     ...                ...               ...      ...   \n",
       "18695      100    10   e4.74                NaN               0.0      0.0   \n",
       "18696      100    10      e5                NaN               0.0      0.0   \n",
       "18697      100    10   e5.33                NaN               0.0      0.0   \n",
       "18698      100    10   e5.66                NaN               0.0      0.0   \n",
       "18699      100    10      e6                NaN               0.0      0.0   \n",
       "\n",
       "       Flow  Od_flow  CollisionRisk  Travel_time  \n",
       "0       0.0     50.0            NaN          0.0  \n",
       "1       0.0     50.0            NaN          0.0  \n",
       "2       0.0     50.0            NaN          0.0  \n",
       "3       0.0     50.0            NaN          0.0  \n",
       "4       0.0     50.0            NaN          0.0  \n",
       "...     ...      ...            ...          ...  \n",
       "18695   0.0   5000.0            NaN          0.0  \n",
       "18696   0.0   5000.0            NaN          0.0  \n",
       "18697   0.0   5000.0            NaN          0.0  \n",
       "18698   0.0   5000.0            NaN          0.0  \n",
       "18699   0.0   5000.0            NaN          0.0  \n",
       "\n",
       "[18700 rows x 10 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "episode              False\n",
       "step                 False\n",
       "edge_id              False\n",
       "Time_to_collision    False\n",
       "Space_mean_speed     False\n",
       "Density              False\n",
       "Flow                 False\n",
       "Od_flow              False\n",
       "CollisionRisk        False\n",
       "Travel_time          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[\"TTC_size\"] =  np.where(df1.Time_to_collision<= TTC_THRESHOLD, \"critical\", \"safe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[\"TTC_threshold\"] = TTC_THRESHOLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_value = df1['Flow'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2648.625461964536"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[\"max_value\"]=max_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
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width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# speed 1 m/s and 5000 odflow\n",
    "#style='darkgrid'\n",
    "import seaborn as sns\n",
    "sns.set_theme(context='notebook', font='sans-serif', style = \"white\",font_scale=1, color_codes=True, rc=None)\n",
    "\n",
    "\n",
    "n=0\n",
    "m=0\n",
    "edges = ['e3.37']\n",
    "xlabel1 = ['Density (veh/m)','Link Flow (veh/hour)', \"Density (veh/m)\"]\n",
    "ylabel1 = ['Link Flow (veh/hour)', 'Space Mean Speed (m/s)', 'Space Mean Speed (m/s)']\n",
    "\n",
    "xlabel2 = ['Link Flow (veh/hour)','Link Flow (veh/hour)','Link Flow (veh/hour)']\n",
    "ylabel2 = ['Time to Collision (s)', 'Time to Collision (s)',\"Travel Time (s)\"]\n",
    "\n",
    "\n",
    "for edge in edges:\n",
    "    fig, axs = plt.subplots(nrows = 1, \n",
    "                        ncols = 3,  \n",
    "                        sharex = False, figsize=(10, 4))\n",
    "    #plt.subplots_adjust(left=0.05, bottom=0.3, right=0.9, top=None, wspace=None, hspace=None)\n",
    "    fig.tight_layout(pad=3.0)\n",
    "    sns.scatterplot(data=df1, x= df1.Density[df1.edge_id == edge], y=df1.Flow[df1.edge_id == edge],ax=axs[0], color=\"palevioletred\")\n",
    "    sns.scatterplot(data=df1, x= df1.Flow[df1.edge_id == edge], y=df1.Space_mean_speed[df1.edge_id == edge],ax=axs[1], color=\"slateblue\")\n",
    "    sns.scatterplot(data=df1, x= df1.Density[df1.edge_id == edge], y=df1.Space_mean_speed[df1.edge_id == edge],ax=axs[2], color=\"cadetblue\")\n",
    "    \n",
    "    #fig.suptitle(\"edge {}\".format(edge), fontweight =\"bold\", fontsize = 14) \n",
    "    n = 0\n",
    "    for x,y in zip(xlabel1,ylabel1):\n",
    "        axs[n].set_xlabel(x)\n",
    "        axs[n].set_ylabel(y)\n",
    "        n += 1\n",
    "    \n",
    "    fig, axs = plt.subplots(nrows = 1, \n",
    "                        ncols = 3,  \n",
    "                        sharex = False, figsize=(10, 4)) \n",
    "    fig.tight_layout(pad=2.0)\n",
    "    sns.scatterplot(data=df1, x= df1.Flow[(df1.edge_id == edge) & (df1.Time_to_collision < 50)], y=df1.Time_to_collision[(df1.edge_id == edge) & (df1.Time_to_collision < 50)],ax=axs[0],\n",
    "                    size= df1.TTC_size,hue = df1.TTC_size, palette=\"viridis\")\n",
    "    sns.scatterplot(data=df1, x= df1.Od_flow[df1.edge_id == edge], y=df1.Time_to_collision[df1.edge_id == edge],ax=axs[1], color=\"olive\")\n",
    "    sns.scatterplot(data=df1, x= df1.Od_flow[df1.edge_id == edge], y=df1.Travel_time[df1.edge_id == edge],ax=axs[2], color=\"olive\")\n",
    "    plt.ylim(0, 300 )\n",
    "    plt.xlim(0, 1600 )\n",
    "    \n",
    "    n = 0\n",
    "    for x,y in zip(xlabel2,ylabel2):\n",
    "        axs[n].set_xlabel(x)\n",
    "        axs[n].set_ylabel(y)\n",
    "        n += 1\n",
    "    \n",
    "    fig, axs = plt.subplots(nrows = 1, \n",
    "                        ncols = 2,  \n",
    "                        sharex = False, figsize=(10,4)) \n",
    "    fig.tight_layout(pad=2.0)\n",
    "    #plt.title(\"edge {}\".format(edge))\n",
    "    \n",
    "    sns.scatterplot(data=df1, x= df1.Od_flow[df1.edge_id == edge], y=df1.CollisionRisk[df1.edge_id == edge],ax=axs[0])\n",
    "    sns.scatterplot(data=df1, x= df1.Travel_time[df1.edge_id == edge], y=df1.Space_mean_speed[df1.edge_id == edge],ax=axs[1])\n",
    "    \n",
    "    \n",
    "    fig, axs = plt.subplots(nrows = 1, \n",
    "                        ncols = 1,  \n",
    "                        sharex = False, figsize=(5,5)) \n",
    "    fig.tight_layout(pad=2.0)\n",
    "    #plt.title(\"edge {}\".format(edge))\n",
    "    \n",
    "    \n",
    "    sns.scatterplot(data=df1, x= df1.Od_flow[df1.edge_id == edge], y=df1.Flow[df1.edge_id == edge],ax=axs)\n",
    "    plt.ylim(0, 5000 )\n",
    "    plt.xlim(0,5000 )\n",
    "\n",
    "    #sns.lineplot(data=df1, x= df1.Od_flow[df1.edge_id == edge], y=df1.max_value[df1.edge_id == edge],ax=axs[2], color=\"r\")\n",
    "\n",
    "    fig.suptitle(\"edge {}\".format(edge), fontweight =\"bold\", fontsize = 14) \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_csv(\"df1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df2 = df1.iloc[0:(int(df1[df1['Od_flow'] == 1650].index[0]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df1[df1['edge_id']== 'e3.37']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>episode</th>\n",
       "      <th>step</th>\n",
       "      <th>edge_id</th>\n",
       "      <th>Time_to_collision</th>\n",
       "      <th>Space_mean_speed</th>\n",
       "      <th>Density</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Od_flow</th>\n",
       "      <th>CollisionRisk</th>\n",
       "      <th>Travel_time</th>\n",
       "      <th>TTC_size</th>\n",
       "      <th>TTC_threshold</th>\n",
       "      <th>max_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.688640</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>19.435981</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.223474</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.148820</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>20.465379</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.906836</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.121762</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>20.404852</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.428629</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.841671</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>19.778303</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.017636</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.261860</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>18.481301</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194.791486</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18623</th>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.425873</td>\n",
       "      <td>0.644622</td>\n",
       "      <td>0.233015</td>\n",
       "      <td>540.743200</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.039012</td>\n",
       "      <td>2496.563987</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18640</th>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.613367</td>\n",
       "      <td>0.623272</td>\n",
       "      <td>0.233636</td>\n",
       "      <td>524.227629</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.037649</td>\n",
       "      <td>2582.084433</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18657</th>\n",
       "      <td>100</td>\n",
       "      <td>8</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.537197</td>\n",
       "      <td>0.627616</td>\n",
       "      <td>0.233636</td>\n",
       "      <td>527.881961</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.036957</td>\n",
       "      <td>2564.209613</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18674</th>\n",
       "      <td>100</td>\n",
       "      <td>9</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.546413</td>\n",
       "      <td>0.639856</td>\n",
       "      <td>0.233636</td>\n",
       "      <td>538.176924</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.031664</td>\n",
       "      <td>2515.158009</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18691</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.066153</td>\n",
       "      <td>0.434953</td>\n",
       "      <td>0.234879</td>\n",
       "      <td>367.781025</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.034271</td>\n",
       "      <td>3700.027758</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1100 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       episode  step edge_id  Time_to_collision  Space_mean_speed   Density  \\\n",
       "8            1     0   e3.37                NaN          8.688640  0.000621   \n",
       "25           1     1   e3.37                NaN          9.148820  0.000621   \n",
       "42           1     2   e3.37                NaN          9.121762  0.000621   \n",
       "59           1     3   e3.37                NaN          8.841671  0.000621   \n",
       "76           1     4   e3.37                NaN          8.261860  0.000621   \n",
       "...        ...   ...     ...                ...               ...       ...   \n",
       "18623      100     6   e3.37           2.425873          0.644622  0.233015   \n",
       "18640      100     7   e3.37           2.613367          0.623272  0.233636   \n",
       "18657      100     8   e3.37           2.537197          0.627616  0.233636   \n",
       "18674      100     9   e3.37           2.546413          0.639856  0.233636   \n",
       "18691      100    10   e3.37           2.066153          0.434953  0.234879   \n",
       "\n",
       "             Flow  Od_flow  CollisionRisk  Travel_time  TTC_size  \\\n",
       "8       19.435981     50.0            NaN   185.223474      safe   \n",
       "25      20.465379     50.0            NaN   175.906836      safe   \n",
       "42      20.404852     50.0            NaN   176.428629      safe   \n",
       "59      19.778303     50.0            NaN   182.017636      safe   \n",
       "76      18.481301     50.0            NaN   194.791486      safe   \n",
       "...           ...      ...            ...          ...       ...   \n",
       "18623  540.743200   5000.0       0.039012  2496.563987  critical   \n",
       "18640  524.227629   5000.0       0.037649  2582.084433  critical   \n",
       "18657  527.881961   5000.0       0.036957  2564.209613  critical   \n",
       "18674  538.176924   5000.0       0.031664  2515.158009  critical   \n",
       "18691  367.781025   5000.0       0.034271  3700.027758  critical   \n",
       "\n",
       "       TTC_threshold    max_value  \n",
       "8                3.5  2735.902635  \n",
       "25               3.5  2735.902635  \n",
       "42               3.5  2735.902635  \n",
       "59               3.5  2735.902635  \n",
       "76               3.5  2735.902635  \n",
       "...              ...          ...  \n",
       "18623            3.5  2735.902635  \n",
       "18640            3.5  2735.902635  \n",
       "18657            3.5  2735.902635  \n",
       "18674            3.5  2735.902635  \n",
       "18691            3.5  2735.902635  \n",
       "\n",
       "[1100 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-3e9cd46e1d6e>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df3.dropna(inplace = True)\n"
     ]
    }
   ],
   "source": [
    "df3.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme(context='notebook', font='sans-serif', style = \"white\",font_scale=1, color_codes=True, rc=None)\n",
    "\n",
    "\n",
    "n=0\n",
    "m=0\n",
    "edges = ['e3.37']\n",
    "xlabel = ['Link Flow (veh/hour)','Travel Time (s)']\n",
    "ylabel = ['Link Flow (veh/hour)', 'Collision Risk']\n",
    "for edge in edges:\n",
    "    fig, axs = plt.subplots(nrows = 1, \n",
    "                        ncols = 2,  \n",
    "                        sharex = False, figsize=(10, 4))\n",
    "    fig.tight_layout(pad=2.0)\n",
    "    plt.subplots_adjust(left=0.08, bottom=0.3, right=0.9, top=None, wspace=None, hspace=None)\n",
    "    sns.scatterplot(data=df3, x= df3.Od_flow[df3.edge_id == edge], y=df3.Travel_time[df3.edge_id == edge],ax=axs[0], color=\"olive\")\n",
    "    #plt.ylim(0, 50000 )\n",
    "    \n",
    "    sns.scatterplot(data=df3, x= df3.Od_flow[df3.edge_id == edge], y=df3.CollisionRisk[df3.edge_id == edge],ax=axs[1], color=\"slateblue\")\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "    #fig.suptitle(\"edge {}\".format(edge), fontweight =\"bold\", fontsize = 14) \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Collision risk vs od_flow\n",
    "#X = df3['Od_flow'].values\n",
    "X = df3['Flow'].values\n",
    "y = df3['CollisionRisk'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.reshape(-1,1)\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   262.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 20 Jan 2021</td> <th>  Prob (F-statistic):</th> <td>4.31e-93</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:44:22</td>     <th>  Log-Likelihood:    </th> <td>  3359.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1043</td>      <th>  AIC:               </th> <td>  -6713.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1040</td>      <th>  BIC:               </th> <td>  -6698.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0032</td> <td>    0.002</td> <td>   -2.100</td> <td> 0.036</td> <td>   -0.006</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td> 7.122e-05</td> <td> 3.79e-06</td> <td>   18.778</td> <td> 0.000</td> <td> 6.38e-05</td> <td> 7.87e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>-4.443e-08</td> <td>  2.1e-09</td> <td>  -21.201</td> <td> 0.000</td> <td>-4.85e-08</td> <td>-4.03e-08</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>34.411</td> <th>  Durbin-Watson:     </th> <td>   0.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  35.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.423</td> <th>  Prob(JB):          </th> <td>2.31e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.692</td> <th>  Cond. No.          </th> <td>5.30e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.3e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.336\n",
       "Model:                            OLS   Adj. R-squared:                  0.334\n",
       "Method:                 Least Squares   F-statistic:                     262.8\n",
       "Date:                Wed, 20 Jan 2021   Prob (F-statistic):           4.31e-93\n",
       "Time:                        17:44:22   Log-Likelihood:                 3359.3\n",
       "No. Observations:                1043   AIC:                            -6713.\n",
       "Df Residuals:                    1040   BIC:                            -6698.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0032      0.002     -2.100      0.036      -0.006      -0.000\n",
       "x1          7.122e-05   3.79e-06     18.778      0.000    6.38e-05    7.87e-05\n",
       "x2         -4.443e-08    2.1e-09    -21.201      0.000   -4.85e-08   -4.03e-08\n",
       "==============================================================================\n",
       "Omnibus:                       34.411   Durbin-Watson:                   0.433\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               35.169\n",
       "Skew:                          -0.423   Prob(JB):                     2.31e-08\n",
       "Kurtosis:                       2.692   Cond. No.                     5.30e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.3e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polynomial_features= PolynomialFeatures(degree=2)\n",
    "xp = polynomial_features.fit_transform(X)\n",
    "model = sm.OLS(y, xp).fit()\n",
    "ypred = model.predict(xp) \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualizing the Polymonial Regression results\n",
    "def viz_polymonial():\n",
    "    fig, ax = plt.subplots(nrows = 1, \n",
    "                        ncols = 1,  \n",
    "                        sharex = False, figsize=(5, 5)) \n",
    "    fig.tight_layout(pad=3.0)\n",
    "    plt.scatter(X, y, color='coral')\n",
    "    plt.plot(X, model.predict(xp), color='darkcyan')\n",
    "    #plt.title('Od flow vs Collision Risk')\n",
    "    plt.xlabel('Link Flow (veh/hour)')\n",
    "    plt.ylabel('Collision Risk')\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
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       "\n",
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       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
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       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
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       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
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       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
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       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
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       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
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       "    x0 = Math.floor(x0) + 0.5;\n",
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       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
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       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz_polymonial()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Link tt vs od_flow\n",
    "#X1 = df3['Od_flow'].values\n",
    "X1 = df3.loc[df3['Density']< 0.052195] ['Flow'].values\n",
    "y1 = df3.loc[df3['Density']< 0.052195] ['Travel_time'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5057    0.052195\n",
       "Name: Density, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.Density[(df3.edge_id == 'e3.37') & (df3.Flow == df3.Flow[df3.edge_id == 'e3.37'].max())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = X1.reshape(-1,1)\n",
    "y1 = y1.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.500</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   60.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 20 Jan 2021</td> <th>  Prob (F-statistic):</th> <td>3.38e-35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:30:30</td>     <th>  Log-Likelihood:    </th> <td> -1066.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   246</td>      <th>  AIC:               </th> <td>   2143.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   241</td>      <th>  BIC:               </th> <td>   2161.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  123.5897</td> <td>   12.919</td> <td>    9.566</td> <td> 0.000</td> <td>   98.140</td> <td>  149.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.1031</td> <td>    0.100</td> <td>    1.036</td> <td> 0.301</td> <td>   -0.093</td> <td>    0.299</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>   -0.0002</td> <td>    0.000</td> <td>   -0.804</td> <td> 0.422</td> <td>   -0.001</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td> 2.238e-07</td> <td> 2.24e-07</td> <td>    0.997</td> <td> 0.320</td> <td>-2.18e-07</td> <td> 6.66e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>-8.004e-11</td> <td> 7.06e-11</td> <td>   -1.134</td> <td> 0.258</td> <td>-2.19e-10</td> <td>  5.9e-11</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>422.012</td> <th>  Durbin-Watson:     </th>  <td>   1.467</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>138055.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 8.927</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>117.674</td> <th>  Cond. No.          </th>  <td>1.37e+13</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.37e+13. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.500\n",
       "Model:                            OLS   Adj. R-squared:                  0.492\n",
       "Method:                 Least Squares   F-statistic:                     60.21\n",
       "Date:                Wed, 20 Jan 2021   Prob (F-statistic):           3.38e-35\n",
       "Time:                        21:30:30   Log-Likelihood:                -1066.6\n",
       "No. Observations:                 246   AIC:                             2143.\n",
       "Df Residuals:                     241   BIC:                             2161.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        123.5897     12.919      9.566      0.000      98.140     149.039\n",
       "x1             0.1031      0.100      1.036      0.301      -0.093       0.299\n",
       "x2            -0.0002      0.000     -0.804      0.422      -0.001       0.000\n",
       "x3          2.238e-07   2.24e-07      0.997      0.320   -2.18e-07    6.66e-07\n",
       "x4         -8.004e-11   7.06e-11     -1.134      0.258   -2.19e-10     5.9e-11\n",
       "==============================================================================\n",
       "Omnibus:                      422.012   Durbin-Watson:                   1.467\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           138055.370\n",
       "Skew:                           8.927   Prob(JB):                         0.00\n",
       "Kurtosis:                     117.674   Cond. No.                     1.37e+13\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.37e+13. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polynomial_features= PolynomialFeatures(degree=4)\n",
    "xp = polynomial_features.fit_transform(X1)\n",
    "model = sm.OLS(y1, xp).fit()\n",
    "ypred = model.predict(xp) \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualizing the Polymonial Regression results\n",
    "def viz_polymonial():\n",
    "    fig, ax = plt.subplots(nrows = 1, \n",
    "                        ncols = 1,  \n",
    "                        sharex = False, figsize=(5, 5)) \n",
    "    fig.tight_layout(pad=3.0)\n",
    "    plt.scatter(X1, y1, color='coral')\n",
    "    plt.plot(X1, model.predict(xp), color='darkcyan')\n",
    "    #plt.title('Od flow vs Collision Risk')\n",
    "    plt.xlabel('Link Flow (veh/hour)')\n",
    "    plt.ylabel('Travel time (s)')\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz_polymonial()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Link tt vs od_flow\n",
    "X1 = df3['Od_flow'].values\n",
    "y1 = df3['Travel_time'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(x, a, b, c, d, e):\n",
    "    return a + b * x + c * x ** 2 + d * x ** 3 + e * x ** 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9.98126284e-12 -1.55302203e-07  8.19352429e-04 -1.04198959e+00\n",
      "  4.67282941e+02]\n",
      "[ 4.67282851e+02 -1.04198934e+00  8.19352248e-04 -1.55302152e-07\n",
      "  9.98125815e-12]\n",
      "[9.10876312e-09 1.71030515e-01 7.81439681e-05 3.36264179e-27\n",
      " 3.11081854e-26]\n"
     ]
    }
   ],
   "source": [
    "print(np.polyfit(X1, y1, 4))\n",
    "popt, _ = curve_fit(func, X1, y1)\n",
    "print(popt)\n",
    "popt_cons, _ = curve_fit(func, X1, y1, bounds=([0, 0, 0, 0, 0], [np.inf, np.inf, np.inf, np.inf,  np.inf]))\n",
    "print(popt_cons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_value = df3['Flow'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1606.7155578742804"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_od_flow = df3.Od_flow[df3.Flow == max_value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5057    1400.0\n",
       "Name: Od_flow, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_od_flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "xnew = np.linspace(X1[0], X1[-1], 1000)\n",
    "plt.plot(X1, y1, 'bo')\n",
    "#plt.plot(xnew, func(xnew, *popt), 'k-')\n",
    "plt.plot(xnew, func(xnew, *popt_cons), 'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(x, a, e):\n",
    "    return a + e * x ** 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9.98126284e-12 -1.55302203e-07  8.19352429e-04 -1.04198959e+00\n",
      "  4.67282941e+02]\n",
      "[5.43005533e+02 4.40073503e-12]\n",
      "[5.43005533e+02 4.40073503e-12]\n"
     ]
    }
   ],
   "source": [
    "print(np.polyfit(X1, y1, 4))\n",
    "popt, _ = curve_fit(func, X1, y1)\n",
    "print(popt)\n",
    "popt_cons, _ = curve_fit(func, X1, y1, bounds=([-np.inf, - np.inf], [np.inf,  np.inf]))\n",
    "print(popt_cons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, \n",
    "                        ncols = 1,  \n",
    "                        sharex = False, figsize=(5, 5)) \n",
    "xnew = np.linspace(X1[0], X1[-1], 10000)\n",
    "plt.plot(X1, y1, 'bo')\n",
    "#plt.plot(xnew, func(xnew, *popt), 'k-')\n",
    "plt.plot(xnew, func(xnew, *popt_cons), 'r-')\n",
    "plt.ylim(0, 5000 )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(x, a, e):\n",
    "    return a + e * x ** 4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func2(t, x, y):\n",
    "    return t[0] + t[1] * x ** 4 - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = np.array([1.0, 0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_lsq = least_squares(func2, x0,loss='soft_l1', f_scale=0.1, args=(X1, y1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.46577418e-11 -6.33008661e-07  2.49264239e-03 -3.11091633e+00\n",
      "  1.18358846e+03]\n",
      "[6.42674202e+02 4.61083069e-12]\n",
      "[6.42674202e+02 4.61083069e-12]\n"
     ]
    }
   ],
   "source": [
    "print(np.polyfit(X1, y1, 4))\n",
    "popt, _ = curve_fit(func, X1, y1)\n",
    "print(popt)\n",
    "popt_cons, _ = curve_fit(func, X1, y1, bounds=([-np.inf, - np.inf], [np.inf,  np.inf]))\n",
    "print(popt_cons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_y(x, a, e, error):\n",
    "    return a + e * x ** 4 + error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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/sfWhDiejQABAgQIEHhHQEB/x0KLAAECBAgQ2IXAuvWb2z3nfBeH7nZXfATbSxu27PY4BxAgQIAAgZwEBPScqm2tBAgQIECgAYHu3hius0tu3vpWZ7v0EyBAgACBLAUE9CzLbtEECBAgQGDPBfrt33vPT9rFGQf03W8Xe+0iQIAAAQL5CQjo+dXcigkQIECAQLcEhgw6IPTq1datc3c+qfeOcQ4Z2G/nbq8JECBAgEDWAgJ61uW3eAIECBAg0HWB/v36hJOOH971E3Zx5MQThoc4no0AAQIECBB4R0BAf8dCiwABAgQIENiNwBknj9rNEV3bPem3j+ragY4iQIAAAQIZCQjoGRXbUgkQIECAQKMCJxw9JBw57KCGhnnvjvOPP/qQhsZwMgECBAgQaEWBHrs7y+bNm8Mtt9wS7rrrrvDUU0+FTZs2hfe85z3hN3/zN8Mf//EfhzPOOCP07t35zWW2bdsW5s+fHxYsWBCWL19enD906NAwduzYcO6554bx48fv1r+VxtjtYh1AgAABAgT2gUBbW1u4ZPLY8MVr7gnduat7vNHc9B3nx3FsBAgQIECAQHuBtu07tvZde/7qV7/6VZg2bVpYsWJFpydPmDAhXH311WHgwIEdjtm4cWO46KKLwpIlSzrsix3x/4hPnTo1zJw5s3J/7GylMTpdZIM7li5dGqZMmVKOMnfu3DBu3LjytQYBAgQIEOiqwP8sfzF89btL9iikx3D+d1MnhDHHHdrVyziOAAECBPaSgGywl2AbHLbhT9DjJ+Wf+cxnwqpVq4qpxE+64yfew4cPDzG433DDDeHpp58uwvfnPve5MGfOnHb/ah7/fWD69OllOD/llFPC5MmTw5AhQ8ITTzwRrrvuurB69epw4403hsGDB4cLL7yww5JbaYwOi9NBgAABAgQSFIghe/ZnTwlX3vxIWLl2425nGL/WHj85Hz1i0G6PdQABAgQIEMhVoOFP0P/1X/81fOMb3yj8Pvaxj4V//ud/bhfA33jjjfCXf/mX4b777iuOueqqq8If/uEflt4LFy4MM2bMKF6fddZZYfbs2eW+2Fi/fn3xqW8M+X379g133nlnGDZsWLtjWmmMdgvr4Rf+layHQQ1HgAABAiH+I/myZ14Ki+5dEe5ftiZs2/bOF/Pio9Ti3drjDeHib859rd0bhgABAukIyAbp1KJ+Jg3fJG7x4sXlePEr6Dv/H9/9998//O3f/m15zE9+8pOyHRvxk/G4DRgwIFx66aVFu/5/Bg0aFC6//PKia+vWrcUn8PX7Y7uVxth5bV4TIECAAIGUBeL/3T9h9JAw88/Hh5u/8kfhX/72tPCNv/7d4u/3drye+anxxf6d//+DlNdkbgQIECBAYF8JNBzQ161bV8w93hDukEOq78h61FHvPErl//7v/8q1xq/FP/7448XrU089NcQwXrXF30nXxrj99tvbHdJKY7RbmBcECBAgQOBdJhCfaz7ysIPCsUceXPz1nPN3WQFNlwABAgT2uUDDAf3QQ///jV5effXVUB++61f27LPPli/rv57+8MMPl/0TJ04s21WNeJO5uMXfo69cubI8pJXGKBelQYAAAQIECBAgQIAAAQLZCTQc0D/84Q+XaLXfopcdOxpvv/12+PrXv152xcet1bb4u/LaNmrUqFqz8u/IkSPL/vgYt9rWSmPU1uQvAQIECBAgQIAAAQIECOQn0PBd3OMd1+PvyuMj0uJzzNesWRPOOeec4i7u8evnN910U/jf//3fQjbe3f13f/d3S+W1a9eW7cMPP7xsVzXiXeFrW/159e13+xi19flLgAABAgQIECBAgAABAvkJNBzQ453V46PQrr/++uJmbQ888ECI/9VvQ4cOLW4UF+/yXr9t2LChfHnggQeW7apG//79y+74zPPa1kpj1NbkLwECBAgQIECAAAECBAjkJ9DwV9wjWfyaeXxmeXwmetUWbyR32223hWeeeabd7vgIttrWr1+/WrPyb/3++vPq2/XHVA1Sv7/+vPp2/THNHqPqevoIECBAgAABAgQIECBAIA+BhgP6f//3fxfPKb/rrrvCwIEDw5e//OXw85//PDz22GPhjjvuCJ/97GdDnz59wt133x3+7M/+LCxbtqyU7d27d9ne3eNX4nNWa1uvXu9Mu5XGqK3PXwIECBAgQIAAAQIECBDIT+CdpNuNtb/44ovhkksuCVu2bAkHH3xwuOWWW0L8TXq8s3t8/nm88dvnP//58G//9m9FSF+/fn24+OKLQ3yeedzqv7Yex9jVVjsnHhPHrm2tNEZtTf4SIECAAAECBAgQIECAQH4CDQX0BQsWlF9rj0H8yCOPrBT84Ac/WHzKHnc+//zzxU3lYrv+d+ebN2+OXZ1u9V+fj5/U17ZWGqO2Jn8JECBAgAABAgQIECBAID+BhgL6L37xi1Ks/nFrZWdd4yMf+Uj56tFHHy3aRxxxRNkX7/6+q61+/2GHHVYe2kpjlIvSIECAAAECBAgQIECAAIHsBBoK6PWfah900EG7xDvkkEPK/bW7sB9zzDFl38qVK8t2VSM+sq22jR49utYMrTRGuSgNAgQIECBAgAABAgQIEMhOoKGAHn93Xtt2F7BfeOGF2qGhFtZPPPHEULs53NKlS8v9VY34nPW4xeehjxgxojyklcYoF6VBgAABAgQIECBAgAABAtkJNBTQJ0yYUIL913/9V9muavzwhz8su8ePH1+0Y9iOATtu8Y7vr732WtHe+X9ieF+xYkXRffrpp7fb3UpjtFuYFwQIECBAgAABAgQIECCQlUBDAX3SpElh8ODBBdhNN90UfvrTn1bixXD+gx/8oNj3vve9L5x88snlceeff37Rjnd4nzVrVti2bVu5LzY2bNhQ9Md2fFzbeeedF5vttlYao93CvCBAgAABAgQIECBAgACBbAR673hu+Ze7u9r4uLMYuG+77bYiWC9atKj4pDs+mzx+Gh6fhX7NNdcU/8XnmPfr169oH3744eUljz322BBvGhe/Iv/LX/4yPPjgg2HAgAHF+YsXLw4zZswIzz33XHH89OnTw2mnnVaeW2u00hi1Ne2Nv/EO+vPmzSuHPvvss0N9LcodGgQIECBAgAABAgQItLSAbJBmedt2BOftjU7trrvuCjNnzuz0K+px/KFDh4Yrr7wy1L7eXn/NGOanTZsWHnroofrudu2pU6cW16j9Zr3dzh0vWmmMndfWU6/jTwWmTJlSDjd37twwbty48rUGAQIECBAgQIAAAQJ5CMgGadZ5v56YVnyE2m/91m+Fm2++OfzsZz8rPkV//fXXQ7yze7zLevzU+5xzzmn33PP668ZPzOfMmRPic9UXLlwYnnzyyRDv9B5vQjdmzJgiVE6cOLH+lA7tVhqjw+J0ECBAgAABAgQIECBAgEDLC/TIJ+gtr9QiC/SvZC1SSMsgQIAAAQIECBAg0KCAbNAg4F46vaGbxO2lORmWAAECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIU2K8nJ/XUU0+F73//++G+++4La9euDdu2bQtHHHFE+J3f+Z0wderUMHz48E4vF4+dP39+WLBgQVi+fHnYtGlTGDp0aBg7dmw499xzw/jx4zs9t7ajlcaorclfAgQIECBAgAABAgQIEMhDoG37jq0nlvov//Iv4ZprrglvvfVW5XADBgwI3/zmN8Pv/d7vddi/cePGcNFFF4UlS5Z02Bc72traioA/c+bMyv2xs5XG6HSRDe5YunRpmDJlSjnK3Llzw7hx48rXGgQIECBAgAABAgQI5CEgG6RZ5x75BP3qq68O3/nOd4oVHnzwweHTn/50GDNmTBHWb7/99vAf//Ef4bXXXguf//znw7x588LRRx9dasR/H5g+fXoZzk855ZQwefLkMGTIkPDEE0+E6667LqxevTrceOONYfDgweHCCy8sz601WmmM2pr8JUCAAAECBAgQIECAAIG8BBr+BD2G6E984hNFGI9fZ7/pppvCyJEj2ynecsst4Utf+lLRd/rpp4dvf/vb5f6FCxeGGTNmFK/POuusMHv27HJfbKxfv7741Pfpp58Offv2DXfeeWcYNmxYu2NaaYx2C+vhF/6VrIdBDUeAAAECBAgQIEDgXSogG6RZuIZvEhfDdvxae/wa+re+9a0O4Twu+0//9E/DscceWwjcfffdYcuWLaVG/GQ8bvEr8JdeemnZX2sMGjQoXH755cXLrVu3hjlz5tR2lX9baYxyURoECBAgQIAAAQIECBAgkJVAQwH9lVdeCT//+c8LsPjJ+Pvf//5O8S644IJwzjnnFF9/jzeAi9uqVavC448/XrRPPfXUEMN41RZ/J33UUUcVu+JX5uu3Vhqjfl3aBAgQIECAAAECBAgQIJCXQEMBPd6t/c033yzEzjzzzF3KffzjHw9f+cpXwt/8zd8UvyWPBz/88MPlORMnTizbVY0JEyYU3fH36CtXriwPaaUxykVpECBAgAABAgQIECBAgEB2Ag0F9CeffLIEq//0PD7u7IUXXgjPPvtseP3118tjdm7E35XXtlGjRtWalX/rf9ceH+dW21ppjNqa/CVAgAABAgQIECBAgACB/AQauot7LSj36dMnHHrooWHdunXF3dxvu+22sGHDhkKzd+/exTPML7744g6P9IrPSq9thx9+eK1Z+bf+Ger159W33+1jVC5cJwECBAgQIECAAAECBAhkIdDQJ+jxDutxizd4e/TRR8OkSZPC97///TKcx31vv/12eOCBB8J5550XbrjhhthVbrUQHzsOPPDAsr+q0b9//7I7PvO8trXSGLU1+UuAAAECBAgQIECAAAEC+Qk0FNBrX1+Pd1efNm1aEczPP//8sGjRovDYY4+FxYsXh5kzZ4YYruOzyv/pn/4p3HrrraXyG2+8Ubb79etXtqsa9fvrz6tv1x/zbhyjas76CBAgQIAAAQIECBAgQCAPgYYC+ubNmwuleFf2+Gl6vAncZZddFkaPHh3233//4nnlf/EXfxHiY9Di1+Dj9rWvfS3EQB+3+PX32hYf07arLQb82tar1zvTbqUxauvzlwABAgQIECBAgAABAgTyE3gn6XZj7fWfWJ900knhk5/8ZOUoJ554YvjEJz5R7Is3j4t3f49b/dfW65+NXuzc6X9qoT52x/Bf21ppjNqa/CVAgAABAgQIECBAgACB/AQaCujxt+e17Q/+4A9qzcq/p512Wtkff68et/rfndc+jS8P2qlRe3Z67B44cGC5t5XGKBelQYAAAQIECBAgQIAAAQLZCTQU0IcOHVqCDRs2rGxXNervsP7KK68UhxxxxBHloWvWrCnbVY36/Ycddlh5SCuNUS5KgwABAgQIECBAgAABAgSyE2gooB933HElWP3d1MvOukb9zdze8573FHuOOeaY8oiVK1eW7arGqlWryu74G/fa1kpj1NbkLwECBAgQIECAAAECBAjkJ9BQQI+/La9tDz/8cK1Z+bf2zPS4c8SIEcUx8fzazeGWLl1aeV6tc8mSJUUzPg+9dn7saKUxamv1lwABAgQIECBAgAABAgTyE2gooMcbw9W+5n7bbbeFdevWdSo4f/78Yl+863rt9+gxbNdC/h133BFee+21yvNjeF+xYkWx7/TTT293TCuN0W5hXhAgQIAAAQIECBAgQIBAVgINBfQYti+44IICLIbrL3zhC6H2bPR6xZtuuincf//9RddHPvKRcOihh5a743PT4xYf0zZr1qywbdu2cl9sxK/Ox/64xUe1nXfeeUW7/n9aaYz6dWkTIECAAAECBAgQIECAQD4Cvb+8Y2tkuR/4wAdC/Hr7r3/96+K/22+/PcTnlL/11lvhl7/8ZfjOd74Trr/++uISgwcPDtdee227x6sde+yxId7VPf4GPR7/4IMPhnh3+Bj4Fy9eHGbMmBGee+654vzp06eXn77Xz7mVxqhfV0+3n3/++TBv3rxy2LPPPjvU37yv3KFBgAABAgQIECBAgEBLC8gGaZa3bfuOrdGpxWeYX3rppSGG8862UaNGhWuuuSbU3+CtdmwM49OmTQsPPfRQravD36lTp4aZM2eWv1nf+YBWGmPntfXU6/hTgSlTppTDzZ07N4wbN658rUGAAAECBAgQIECAQB4CskGadd6vJ6bVr1+/cNVVV4X77rsv/OAHPwiPPPJI8Xv0gw46KLz3ve8NZ555Zvj4xz/e7rnn9deNn5jPmTMnLFiwICxcuDA8+eSTYePGjeHggw8OY8aMKULlxIkT60/p0G6lMTosTgcBAgQIECBAgAABAgQItLxAj3yC3vJKLbJA/0rWIoW0DAIECBAgQIAAAQINCsgGDQLupdMbukncXpqTYQkQIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRQEBPcWqmBMBAgQIECBAgAABAgQIZCcgoGdXcgsmQIAAAQIECBAgQIAAgRQFBPQUq2JOBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAgQIAAAQIECGQnIKBnV3ILJkCAAAECBAgQIECAAIEUBQT0FKtiTgQIECBAgAABAgQIECCQnYCAnl3JLZgAAQIECBAgQIAAAQIEUhQQ0FOsijkRIECAAAECBAgQIECAQHYCAnp2JbdgAgQIECBAgAABAgQIEEhRQEBPsSrmRIAAAQIECBAgQIAAAQLZCQjo2ZXcggkQIECAAAECBAgQIEAgRYH99uakXn311TBp0qTw4osvho9+9KPh61//eqeX27ZtW5g/f35YsGBBWL58edi0aVMYOnRoGDt2bDj33HPD+PHjOz23tqOVxqityV8CBAgQIECAAAECBAgQyENgrwb0r3zlK0U43x3lxo0bw0UXXRSWLFnS7tDnn38+xP8WLVoUpk6dGmbOnNluf/2LVhqjfl3aBAgQIECAAAECBAgQIJCHwF4L6D/+8Y/DwoULd6u4ffv2MH369DKcn3LKKWHy5MlhyJAh4YknngjXXXddWL16dbjxxhvD4MGDw4UXXthhzFYao8PidBAgQIAAAQIECBAgQIBAFgJ75TfoL7/8cpg1a1aXAH/4wx+Ge+65pzj2rLPOCtdff334/d///XDiiScWQX3evHlh9OjRxf6rr746rF27tsO4rTRGh8XpIECAAAECBAgQIECAAIEsBPZKQL/88svDunXrik+8d6cYPxmP24ABA8Kll17a4fBBgwaFOF7ctm7dGubMmdPhmFYao8PidBAgQIAAAQIECBAgQIBAFgI9HtBvvfXWcPvtt4devXqFyy67bJeIq1atCo8//nhxzKmnnhpiGK/axo0bF4466qhiVxy7fmulMerXpU2AAAECBAgQIECAAAECeQn0aECPn5pfccUVhWC8qdsHPvCBXWo+/PDD5f6JEyeW7arGhAkTiu74e/SVK1eWh7TSGOWiNAgQIECAAAECBAgQIEAgO4EeDejxd+evvPJK8Wl3vPHb7rann366PGTUqFFlu6oxcuTIsvupp54q2600RrkoDQIECBAgQIAAAQIECBDITqDHAnp8fnm8c3v8avvs2bND3759d4tZf8O3ww8/fJfHDx8+vNxff159+90+RrlADQIECBAgQIAAAQIECBDITqBHAvoLL7wQ/vEf/7HAi19tHzNmTJcgN2zYUB534IEHlu2qRv/+/cvu+Mzz2tZKY9TW5C8BAgQIECBAgAABAgQI5CfQIwE93gzu1VdfDfFr6n/913/dZcU33nijPLZfv35lu6pRv7/+vPp2/THvxjGq5qyPAAECBAgQIECAAAECBPIQaDig/+d//mf42c9+Vn61fXchuZ61d+/e5cu2trayXdXYvn172R2/Rl/bWmmM2pr8JUCAAAECBAgQIECAAIH8BN5Jut1Y+/PPPx++9rWvFWf++Z//eRg7duwejVL/tfUtW7bs8tz4DPTatv/++9eaoZXGKBelQYAAAQIECBAgQIAAAQLZCXQ7oMdPtP/+7/8+vPbaa8VX27ty1/addet/d7558+add7d7vWnTpvL1wIEDy3YrjVEuSoMAAQIECBAgQIAAAQIEshPYr7srvuWWW8J9991XnP6pT30qrFixosNQL774YtkXf6P+xBNPFK+HDBkShg4dGo444ohy/5o1a8Jhhx1Wvt65EffXtvrjWmmM2vr8JUCAAAECBAgQIECAAIH8BLod0B999NFS64orrijbnTUWL14c4n9x+9znPhcuvvjicMwxx5SHr1y5Mpx44onl650bq1atKrtGjx5dtltpjHJRGgQIECBAgAABAgQIECCQnUC3v+LeE1IxkNduDrd06dJdDrlkyZJif3we+ogRI8pjW2mMclEaBAgQIECAAAECBAgQIJCdQLcDerw53PLly3f5309+8pMS9KMf/Wh5bPz0PG4xbNc+Nb/jjjuK37OXJ9Q1YnivfYX+9NNPr9vTWmO0W5gXBAgQIECAAAECBAgQIJCVQLcDek8pnX/++cVQ69evD7NmzQrbtm1rN/SGDRuK/tjZp0+fcN5557XbH1+00hgdFqeDAAECBAgQIECAAAECBLIQ6PZv0HtKZ9KkSWHevHnhnnvuCT/60Y/C2rVrQ7zpXLwRXPyE/tprrw2rV68uLhc/eR85cmSHS7fSGB0Wp4MAAQIECBAgQIAAAQIEshDY5wE9Kl911VVh2rRp4aGHHgrx6+xVv0efOnVquPDCCzstSiuN0eki7SBAgAABAgQIECBAgACBlhVIIqAPGDAgzJkzJyxYsCAsXLgwPPnkk2Hjxo3h4IMPDmPGjAlTpkwJEydO3GURWmmMXS7UTgIECBAgQIAAAQIECBBoSYG27Tu2llyZRXUQiN9MiDRjuXoAAEAASURBVP/YUdvmzp0bxo0bV3vpLwECBAgQIECAAAECmQjIBmkWep/fJC5NFrMiQIAAAQIECBAgQIAAAQLNFRDQm+vtagQIECBAgAABAgQIECBAoFJAQK9k0UmAAAECBAgQIECAAAECBJorIKA319vVCBAgQIAAAQIECBAgQIBApYCAXsmikwABAgQIECBAgAABAgQINFdAQG+ut6sRIECAAAECBAgQIECAAIFKAQG9kkUnAQIECBAgQIAAAQIECBBoroCA3lxvVyNAgAABAgQIECBAgAABApUCAnoli04CBAgQIECAAAECBAgQINBcAQG9ud6uRoAAAQIECBAgQIAAAQIEKgUE9EoWnQQIECBAgAABAgQIECBAoLkCAnpzvV2NAAECBAgQIECAAAECBAhUCgjolSw6CRAgQIAAAQIECBAgQIBAcwUE9OZ6uxoBAgQIECBAgAABAgQIEKgUENArWXQSIECAAAECBAgQIECAAIHmCgjozfV2NQIECBAgQIAAAQIECBAgUCkgoFey6CRAgAABAgQIECBAgAABAs0VENCb6+1qBAgQIECAAAECBAgQIECgUkBAr2TRSYAAAQIECBAgQIAAAQIEmisgoDfX29UIECBAgAABAgQIECBAgEClgIBeyaKTAAECBAgQIECAAAECBAg0V0BAb663qxEgQIAAAQIECBAgQIAAgUoBAb2SRScBAgQIECBAgAABAgQIEGiugIDeXG9XI0CAAAECBAgQIECAAAEClQICeiWLTgIECBAgQIAAAQIECBAg0FwBAb253q5GgAABAgQIECBAgAABAgQqBQT0ShadBAgQIECAAAECBAgQIECguQICenO9XY0AAQIECBAgQIAAAQIECFQKCOiVLDoJECBAgAABAgQIECBAgEBzBQT05nq7GgECBAgQIECAAAECBAgQqBQQ0CtZdBIgQIAAAQIECBAgQIAAgeYKCOjN9XY1AgQIECBAgAABAgQIECBQKSCgV7LoJECAAAECBAgQIECAAAECzRUQ0Jvr7WoECBAgQIAAAQIECBAgQKBSQECvZNFJgAABAgQIECBAgAABAgSaKyCgN9fb1QgQIECAAAECBAgQIECAQKWAgF7JopMAAQIECBAgQIAAAQIECDRXQEBvrrerESBAgAABAgQIECBAgACBSgEBvZJFJwECBAgQIECAAAECBAgQaK6AgN5cb1cjQIAAAQIECBAgQIAAAQKVAgJ6JYtOAgQIECBAgAABAgQIECDQXAEBvbnerkaAAAECBAgQIECAAAECBCoFBPRKFp0ECBAgQIAAAQIECBAgQKC5AgJ6c71djQABAgQIECBAgAABAgQIVAoI6JUsOgkQIECAAAECBAgQIECAQHMFBPTmersaAQIECBAgQIAAAQIECBCoFBDQK1l0EiBAgAABAgQIECBAgACB5goI6M31djUCBAgQIECAAAECBAgQIFApIKBXsugkQIAAAQIECBAgQIAAAQLNFRDQm+vtagQIECBAgAABAgQIECBAoFJAQK9k0UmAAAECBAgQIECAAAECBJorIKA319vVCBAgQIAAAQIECBAgQIBApYCAXsmikwABAgQIECBAgAABAgQINFdAQG+ut6sRIECAAAECBAgQIECAAIFKAQG9kkUnAQIECBAgQIAAAQIECBBoroCA3lxvVyNAgAABAgQIECBAgAABApUCAnoli04CBAgQIECAAAECBAgQINBcAQG9ud6uRoAAAQIECBAgQIAAAQIEKgUE9EoWnQQIECBAgAABAgQIECBAoLkCAnpzvV2NAAECBAgQIECAAAECBAhUCgjolSw6CRAgQIAAAQIECBAgQIBAcwUE9OZ6uxoBAgQIECBAgAABAgQIEKgUENArWXQSIECAAAECBAgQIECAAIHmCgjozfV2NQIECBAgQIAAAQIECBAgUCkgoFey6CRAgAABAgQIECBAgAABAs0VENCb6+1qBAgQIECAAAECBAgQIECgUkBAr2TRSYAAAQIECBAgQIAAAQIEmisgoDfX29UIECBAgAABAgQIECBAgEClgIBeyaKTAAECBAgQIECAAAECBAg0V0BAb663qxEgQIAAAQIECBAgQIAAgUoBAb2SRScBAgQIECBAgAABAgQIEGiugIDeXG9XI0CAAAECBAgQIECAAAEClQICeiWLTgIECBAgQIAAAQIECBAg0FwBAb253q5GgAABAgQIECBAgAABAgQqBQT0ShadBAgQIECAAAECBAgQIECguQICenO9XY0AAQIECBAgQIAAAQIECFQKCOiVLDoJECBAgAABAgQIECBAgEBzBQT05nq7GgECBAgQIECAAAECBAgQqBQQ0CtZdBIgQIAAAQIECBAgQIAAgeYKCOjN9XY1AgQIECBAgAABAgQIECBQKSCgV7LoJECAAAECBAgQIECAAAECzRUQ0Jvr7WoECBAgQIAAAQIECBAgQKBSQECvZNFJgAABAgQIECBAgAABAgSaKyCgN9fb1QgQIECAAAECBAgQIECAQKWAgF7JopMAAQIECBAgQIAAAQIECDRXQEBvrrerESBAgAABAgQIECBAgACBSgEBvZJFJwECBAgQIECAAAECBAgQaK6AgN5cb1cjQIAAAQIECBAgQIAAAQKVAgJ6JYtOAgQIECBAgAABAgQIECDQXAEBvbnerkaAAAECBAgQIECAAAECBCoFBPRKFp0ECBAgQIAAAQIECBAgQKC5AgJ6c71djQABAgQIECBAgAABAgQIVAoI6JUsOgkQIECAAAECBAgQIECAQHMFBPTmersaAQIECBAgQIAAAQIECBCoFBDQK1l0EiBAgAABAgQIECBAgACB5goI6M31djUCBAgQIECAAAECBAgQIFApIKBXsugkQIAAAQIECBAgQIAAAQLNFRDQm+vtagQIECBAgAABAgQIECBAoFJAQK9k0UmAAAECBAgQIECAAAECBJorIKA319vVCBAgQIAAAQIECBAgQIBApYCAXsmikwABAgQIECBAgAABAgQINFdAQG+ut6sRIECAAAECBAgQIECAAIFKgf0qe3USIECAAAECBAgQIECAQAeBTVveDOvWbw5b3ng79Nu/dxgy6IDQv1+fDsfpINAdAQG9O2rOIUCAAAECBAgQIEAgG4Ht27eHx55ZFxbduyI8sGxt2LZte7n2Xr3awknHDw9nnDwqnHD0kNDW1lbu0yCwpwIC+p6KOZ4AAQIECBAgQIAAgWwEnv71+nDlzY+ElWs3Vq45hvV7f/F88d+Rww4Kl0weG0aPGFR5rE4CuxPwG/TdCdlPgAABAgQIECBAgECWAv+z/MXwxWvu6TSc74wSQ3w8Pp5nI9AdAQG9O2rOIUCAAAECBAgQIECgpQXiJ+df/e6S4rfme7LQ+Nv0eF4830ZgTwUE9D0VczwBAgQIECBAgAABAi0tEH9zHr/WHsN2d7Z43rd2nB/HsRHYEwEBfU+0HEuAAAECBAgQIECAQMsLxBvCdfab864u/rkdX3df9sxLXT3ccQQKAQHdG4EAAQIECBAgQIAAAQJ1Arfe+6u6V91vLrpvRfdPdmaWAgJ6lmW3aAIECBAgQIAAAQIEqgTic87vX7amatce993/2JoQx7MR6KqAgN5VKccRIECAAAECBAgQINDyAuvWb273nPNGFhwfwfbShi2NDOHczAQE9MwKbrkECBAgQIAAAQIECHQu0N0bw3U24uatb3W2Sz+BDgICegcSHQQIECBAgAABAgQI5CrQb//ePbr0A/ru16PjGay1BQT01q6v1REgQIAAAQIECBAgsAcCQwYdEHr1atuDMzo/tPeOcQ4Z2K/zA+whsJOAgL4TiJcECBAgQIAAAQIECOQr0L9fn3DS8cN7BGDiCcNDHM9GoKsCAnpXpRxHgAABAgQIECBAgEAWAmecPKpH1jnpt4/qkXEMko+AgJ5Pra2UAAECBAgQIECAAIEuCJxw9JBw5LCDunBk54e8d8f5xx99SOcH2EOgQkBAr0DRRYAAAQIECBAgQIBAvgJtbW3hksljQ3dvGBfPm77j/DiOjcCeCAjoe6LlWAIECBAgQIAAAQIEshAYPWJQ+LupE/Y4pMdwHs+L59sI7KmAgL6nYo4nQIAAAQIECBAgQCALgTHHHRpmf/aULn/dPX6tPR4fz7MR6I6Ah/J1R805BAgQIECAAAECBAhkIRA/Cb/6C6eGZc+8FBbduyLcv2xN2LZte7n2+Ci1eLf2eEO4+JtzX2svaTS6ISCgdwPNKQQIECBAgAABAgQI5CMQQ/cJo4cU/23a8mZ4acOWsHnrW+GAvvsVzzn3KLV83gt7e6UC+t4WNj4BAgQIECBAgAABAi0jEMO4QN4y5UxuIX6DnlxJTIgAAQIECBAgQIAAAQIEchTwCXqOVbdmAgQIECBAoKUE4ldu163fHLa88XZxx+khgw7wCV9LVdhiCBDIRUBAz6XS1kmAAAECBAi0lMD27dvDY8+sK25a9cCyte1uWtVrx02rTjp+eDjj5FHhhKOHuGlVS1XeYggQaGUBAb2Vq2ttBAgQIECAQEsKPP3r9eHKmx8JK9durFxfvMP0vb94vvjvyB2Pfbpk8ljPZK6U0kmAAIG0BPwGPa16mA0BAgQIECBAYJcC/7P8xfDFa+7pNJzvfHIM8fH4eJ6NAAECBNIWENDTro/ZESBAgAABAgRKgfjJ+Ve/u6T4rXnZ2YVG/G16PC+ebyNAgACBdAUE9HRrY2YECBAgQIAAgVIg/uY8fq09hu3ubPG8b+04P45jI0CAAIE0BQT0NOtiVgQIECBAgACBdgLxhnCd/ea83YG7ePHcjq+7L3vmpV0cYRcBAgQI7EsBAX1f6rs2AQIECBAgQKCLArfe+6suHrnrwxbdt2LXB9hLgAABAvtMQEDfZ/QuTIAAAQIECBDomkB8zvn9y9Z07eDdHHX/Y2tCHM9GgAABAukJCOjp1cSMCBAgQIAAAQLtBNat39zuOeftdu7hi/gItpc2bNnDsxxOgAABAs0QENCboewaBAgQIECAAIEGBLp7Y7jOLrl561ud7dJPgAABAvtQQEDfh/guTYAAAQIECBDoikC//Xt35bAuH3NA3/26fKwDCRAgQKB5AgJ686xdiQABAgQIECDQLYEhgw4IvXq1devcnU/qvWOcQwb227nbawIECBBIQEBAT6AIpkCAAAECBAgQ2JVA/359wknHD9/VIV3eN/GE4SGOZyNAgACB9AQE9PRqYkYECBAgQIAAgQ4CZ5w8qkNfdzom/fZR3TnNOQQIECDQBAEBvQnILkGAAAECBAgQaFTghKOHhCOHHdTQMO/dcf7xRx/S0BhOJkCAAIG9JyCg7z1bIxMgQIAAAQIEekygra0tXDJ5bOjuDePiedN3nB/HsREgQIBAmgICepp1MSsCBAgQIECAQAeB0SMGhb+bOmGPQ3oM5/G8eL6NAAECBNIVENDTrY2ZESBAgAABAgQ6CIw57tAw+7OndPnr7vFr7fH4eJ6NAAECBNIW8BDMtOtjdgQIECBAgACBDgLxk/Crv3BqWPbMS2HRvSvC/cvWhG3btpfHxUepxbu1xxvCxd+c+1p7SaNBgACBpAUE9KTLY3IECBAgQIAAgWqBGLpPGD2k+G/TljfDSxu2hM1b3woH9N2veM65R6lVu+klQIBAygICesrVMTcCBAgQIECAQBcEYhgXyLsA5RACBAgkLuA36IkXyPQIECBAgAABAgQIECBAIA+BHvsEfd26deHmm28O99xzT1ixYkXYtGlTGDBgQDjmmGPChz/84XDOOeeE/v37d6q6bdu2MH/+/LBgwYKwfPny4vyhQ4eGsWPHhnPPPTeMHz++03NrO1ppjNqa/CVAgAABAgQIECBAgACBPATatu/YGl3qj3/84zBz5sywcePGToc64ogjwjXXXBN+4zd+o8Mx8byLLrooLFmypMO+2BF/YzV16tTiGpUH7OhspTE6W2Oj/UuXLg1Tpkwph5k7d24YN25c+VqDAAECBAgQIECAAIE8BGSDNOvc8CfoMVRPnz49vPnmm6FPnz7FJ+Uf+tCHwqBBg8KaNWuKT8V/+tOfhtWrV4dPf/rTYd68eWH48OGlRvz3gXh+LZyfcsopYfLkyWHIkCHhiSeeCNddd11x7o033hgGDx4cLrzwwvLcWqOVxqityV8CBAgQIECAAAECBAgQyEugoU/QYzA+88wzw9NPP12E8+uvvz588IMf7CAYPzn/9re/XfTH47/xjW+UxyxcuDDMmDGjeH3WWWeF2bNnl/tiY/369cWnvvEaffv2DXfeeWcYNmxYu2NaaYx2C+vhF/6VrIdBDUeAAAECBAgQIEDgXSogG6RZuIZuEvfoo48W4TwuLf5OvCqcx33x6+vHHntsbBYBO/4+vbbFT8bjFn+vfumll9a6y7/xk/jLL7+8eL1169YwZ86ccl+t0Upj1NbkLwECBAgQIECAAAECBAjkJdBQQH/ooYdKrXgjuM62+Bvyk08+udj9xhtvhGeffbZor1q1Kjz++ONF+9RTTy2+Fl81Rvyd9FFHHVXsuv3229sd0kpjtFuYFwQIECBAgAABAgQIECCQlUBDAf39739/mDZtWviTP/mTMkB3pld/L7r4SXjcHn744fLwiRMnlu2qxoQJE4ru+Fv2lStXloe00hjlojQIECBAgAABAgQIECBAIDuBhm4SF0P17oJ1TfTBBx+sNUO8o3vc4u/Ka9uoUaNqzcq/I0eOLPufeuqpcOSRRxavW2mMcoEaBAgQIECAAAECBAgQIJCdQEOfoHdVa/HixcUd2ePx8bfotZu8rV27thzi8MMPL9tVjfo7v9efV99+t49RtW59BAgQIECAAAECBAgQIJCHwF4P6C+//HKYNWtWqXnBBReU7Q0bNpTtAw88sGxXNfr371921z9vvZXGKBeoQYAAAQIECBAgQIAAAQLZCezVgP7666+Hv/qrvyqehx5l4+/IP/axj5XI8YZxta1fv361ZuXf+v3159W364+pGqR+f/159e36Y5o9RtX19BEgQIAAAQIECBAgQIBAHgJ7LaDHT7k/85nPhPgotrjFr7V/85vfDL16vXPJ3r17l8rxTu+72upvMteqY+xq/fYRIECAAAECBAgQIECAQGsLvJOWe3CdL774Yjj//PPDI488Uow6ZMiQcMMNN4ShQ4e2u0r919a3bNnSbt/OL2p3fo/9+++/f7m7lcYoF6VBgAABAgQIECBAgAABAtkJ9HhAf/LJJ8MnP/nJ8qZw8ZPzOXPmhKOPProDbv3vzjdv3txhf33Hpk2bypcDBw4s2600RrkoDQIECBAgQIAAAQIECBDITqBHA3q8W/vkyZND7c7q73vf+8L3vve9ynAepWuPW4vtNWvWxD+dbvX7DzvssPK4VhqjXJQGAQIECBAgQIAAAQIECGQn0GMBff78+eGiiy4KtU+6x44dG26++eZ2IXxn3WOOOabsWrlyZdmuaqxatarsHj16dNlupTHKRWkQIECAAAECBAgQIECAQHYCPRLQ582bF774xS+Gt956qwD8oz/6o3DTTTeFQYMG7RL0xBNPDLWbwy1dunSXxy5ZsqTYH5+HPmLEiPLYVhqjXJQGAQIECBAgQIAAAQIECGQn0HBAf+ihh8Jll10WandZP++888KVV17Z7kZunanGsB0DdtzuuOOO8Nprr1UeGsP7ihUrin2nn356u2NaaYx2C/OCAAECBAgQIECAAAECBLISaCigx0A9Y8aM8PbbbxdoZ599dviHf/iH8lPxrkjGu73Hbf369WHWrFlh27Zt7U7bsGFD0R87+/TpE+I/AOy8tdIYO6/NawIECBAgQIAAAQIECBDIQ2C/Rpb57//+7+XN3eIj1M4555zy7u27Gjd+6l37+vukSZNC/Ir8PffcE370ox8VN5j71Kc+FeKN4JYvXx6uvfbasHr16mK4iy++OIwcObLD0K00RofF6SBAgAABAgQIECBAgACBLATadnw1fXt3V/qhD32oDOh7Msbs2bPDWWedVZ4SP4mfNm1aiF+X72ybOnVqmDlzZqefzrfSGJ0ZNNoffyowZcqUcpi5c+eGcePGla81CBAgQIAAAQIECBDIQ0A2SLPO3f4E/eWXX+5WOK9iGDBgQPGs9AULFoSFCxeG+Cz1jRs3hoMPPjiMGTOmCJUTJ06sOrXsa6UxykVpECBAgAABAgQIECBAgEA2Ag19gp6NUoss1L+StUghLYMAAQIECBAgQIBAgwKyQYOAe+n0hm4St5fmZFgCBAgQIECAAAECBAgQIJCdgICeXcktmAABAgQIECBAgAABAgRSFBDQU6yKOREgQIAAAQIECBAgQIBAdgICenYlt2ACBAgQIECAAAECBAgQSFFAQE+xKuZEgAABAgQIECBAgAABAtkJCOjZldyCCRAgQIAAAQIECBAgQCBFAQE9xaqYEwECBAgQIECAAAECBAhkJyCgZ1dyCyZAgAABAgQIECBAgACBFAUE9BSrYk4ECBAgQIAAAQIECBAgkJ2AgJ5dyS2YAAECBAgQIECAAAECBFIUENBTrIo5ESBAgAABAgQIECBAgEB2AgJ6diW3YAIECBAgQIAAAQIECBBIUUBAT7Eq5kSAAAECBAgQIECAAAEC2QkI6NmV3IIJECBAgAABAgQIECBAIEUBAT3FqpgTAQIECBAg8P/auxM4Kap70eP/GQQGhGGAkU1AdjdAQEBkuYqoRIlEzSYxKu/lfozRmKiJirnxGr0vmptr5ObF5BPjR01IiElMhBDxuRE3EESWBAYRBQaHHRmYGXCGdXj1L2+VvXdXT1X36a5ffT5DV1edOnXO9xQz/e86dQ4CCCCAAAIIhE6AAD10TU6FEUAAAQQQQAABBBBAAAEETBQgQDexVSgTAggggAACCCCAAAIIIIBA6AQI0EPX5FQYAQQQQAABBBBAAAEEEEDARAECdBNbhTIhgAACCCCAAAIIIIAAAgiEToAAPXRNToURQAABBBBAAAEEEEAAAQRMFCBAN7FVKBMCCCCAAAIIIIAAAggggEDoBAjQQ9fkVBgBBBBAAAEEEEAAAQQQQMBEAQJ0E1uFMiGAAAIIIIAAAggggAACCIROgAA9dE1OhRFAAAEEEEAAAQQQQAABBEwUIEA3sVUoEwIIIIAAAggggAACCCCAQOgECNBD1+RUGAEEEEAAAQQQQAABBBBAwEQBAnQTW4UyIYAAAggggAACCCCAAAIIhE6AAD10TU6FEUAAAQQQQAABBBBAAAEETBQgQDexVSgTAggggAACCCCAAAIIIIBA6AQI0EPX5FQYAQQQQAABBBBAAAEEEEDARAECdBNbhTIhgAACCCCAAAIIIIAAAgiEToAAPXRNToURQAABBBBAAAEEEEAAAQRMFCBAN7FVKBMCCCCAAAIIIIAAAggggEDoBAjQQ9fkVBgBBBBAAAEEEEAAAQQQQMBEAQJ0E1uFMiGAAAIIIIAAAggggAACCIROgAA9dE1OhRFAAAEEEEAAAQQQQAABBEwUIEA3sVUoEwIIIIAAAggggAACCCCAQOgECNBD1+RUGAEEEEAAAQQQQAABBBBAwEQBAnQTW4UyIYAAAggggAACCCCAAAIIhE6AAD10TU6FEUAAAQQQQAABBBBAAAEETBQgQDexVSgTAggggAACCCCAAAIIIIBA6AQI0EPX5FQYAQQQQAABBBBAAAEEEEDARAECdBNbhTIhgAACCCCAAAIIIIAAAgiEToAAPXRNToURQAABBBBAAAEEEEAAAQRMFCBAN7FVKBMCCCCAAAIIIIAAAggggEDoBAjQQ9fkVBgBBBBAAAEEEEAAAQQQQMBEAQJ0E1uFMiGAAAIIIIAAAggggAACCIROgAA9dE1OhRFAAAEEEEAAAQQQQAABBEwUIEA3sVUoEwIIIIAAAggggAACCCCAQOgECNBD1+RUGAEEEEAAAQQQQAABBBBAwEQBAnQTW4UyIYAAAggggAACCCCAAAIIhE6AAD10TU6FEUAAAQQQQAABBBBAAAEETBQgQDexVSgTAggggAACCCCAAAIIIIBA6AQI0EPX5FQYAQQQQAABBBBAAAEEEEDARAECdBNbhTIhgAACCCCAAAIIIIAAAgiEToAAPXRNToURQAABBBBAAAEEEEAAAQRMFCBAN7FVKBMCCCCAAAIIIIAAAggggEDoBAjQQ9fkVBgBBBBAAAEEEEAAAQQQQMBEAQJ0E1uFMiGAAAIIIIAAAggggAACCIROgAA9dE1OhRFAAAEEEEAAAQQQQAABBEwUIEA3sVUoEwIIIIAAAggggAACCCCAQOgECNBD1+RUGAEEEEAAAQQQQAABBBBAwEQBAnQTW4UyIYAAAggggAACCCCAAAIIhE6AAD10TU6FEUAAAQQQQAABBBBAAAEETBQgQDexVSgTAggggAACCCCAAAIIIIBA6AQI0EPX5FQYAQQQQAABBBBAAAEEEEDARAECdBNbhTIhgAACCCCAAAIIIIAAAgiEToAAPXRNToURQAABBBBAAAEEEEAAAQRMFCBAN7FVKBMCCCCAAAIIIIAAAggggEDoBAjQQ9fkVBgBBBBAAAEEEEAAAQQQQMBEAQJ0E1uFMiGAAAIIIIAAAggggAACCIROgAA9dE1OhRFAAAEEEEAAAQQQQAABBEwUIEA3sVUoEwIIIIAAAggggAACCCCAQOgECNBD1+RUGAEEEEAAAQQQQAABBBBAwEQBAnQTW4UyIYAAAggggAACCCCAAAIIhE6AAD10TU6FEUAAAQQQQAABBBBAAAEETBQgQDexVSgTAggggAACCCCAAAIIIIBA6AQI0EPX5FQYAQQQQAABBBBAAAEEEEDARAECdBNbhTIhgAACCCCAAAIIIIAAAgiEToAAPXRNToURQAABBBBAAAEEEEAAAQRMFCBAN7FVKBMCCCCAAAIIIIAAAggggEDoBAjQQ9fkVBgBBBBAAAEEEEAAAQQQQMBEAQJ0E1uFMiGAAAIIIIAAAggggAACCIROgAA9dE1OhRFAAAEEEEAAAQQQQAABBEwUIEA3sVUoEwIIIIAAAggggAACCCCAQOgECNBD1+RUGAEEEEAAAQQQQAABBBBAwEQBAnQTW4UyIYAAAggggAACCCCAAAIIhE6AAD10TU6FEUAAAQQQQAABBBBAAAEETBQgQDexVSgTAggggAACCCCAAAIIIIBA6AQI0EPX5FQYAQQQQAABBBBAAAEEEEDARAECdBNbhTIhgAACCCCAAAIIIIAAAgiETuCk0NWYCiOAAAIIIIAAAgi4Ao2HjsreuiY5dOS4lLVpJZUV7aR9WWt3PysIIIAAArkTIEDPnTVnQgABBBBAAAEEjBA4ceKErN20VxYuqZZlVbukufmEW67S0hI5f2hPuXxCPxk2sFJKSkrcfawggAACCAQrQIAerC+5I4AAAggggAACRgls3FYns59eJTW7DiQslwbrS9bssH/69ugot88YJYN6VyRMy0YEEEAAAX8FeAbdX09yQwABBBBAAAEEjBVYvWGP3PPzxUmD89iCaxCv6fU4FgQQQACB4AUI0IM35gwIIIAAAggggEDeBfTO+YO/Xm4/a+6lMPpsuh6nx7MggAACCAQrQIAerC+5I4AAAggggAACeRfQZ861W7sG29ksetx/W8drPiwIIIAAAsEJEKAHZ0vOCCCAAAIIIICAEQI6IFyyZ84zLeCHVnf3qk21mSYnHQIIIIBAFgIE6FmgcQgCCCCAAAIIIFBIAs8v2eJLcRe+Ve1LPmSCAAIIIJBYgAA9sQtbEUAAAQQQQACBohDQec6XVu30pS5L1+4UzY8FAQQQQCAYAQL0YFzJFQEEEEAAAQQQMEJgb11T1DznLSmUTsFWW3+oJVlwLAIIIIBACgEC9BQ47EIAAQQQQAABBApdINuB4ZLVu+nwsWS72I4AAggg0EIBAvQWAnI4AggggAACCCBgskBZm1a+Fq9d25N8zY/MEEAAAQQ+FSBA/9SCNQQQQAABBBBAoOgEKivaSWlpiS/1amXl07VTmS95kQkCCCCAQLwAAXq8CVsQQAABBBBAAIGiEWhf1lrOH9rTl/qMG9ZTND8WBBBAAIFgBAjQg3ElVwQQQAABBBBAwBiByyf086Us08b39yUfMkEAAQQQSCxAgJ7Yha0IIIAAAggggEDRCAwbWCl9e3RsUX1Os44fOrBri/LgYAQQQACB1AIE6Kl92IsAAggggAACCBS8QElJidw+Y5RkO2CcHnebdbzmw4IAAgggEJwAAXpwtuSMAAIIIIAAAggYIzCod4V8b+ZYz0G6Bud6nB7PggACCCAQrAABerC+5I4AAggggAACCBgjMPL0bvLQLRMz7u6u3do1vR7HggACCCAQvAATWQZvzBkQQAABBBBAAAFjBPRO+KPfnSxVm2pl4ZJqWVq1U5qbT7jl06nUdLR2HRBOnzmnW7tLwwoCCCAQuAABeuDEnAABBBBAAAEEEDBLQIPuYYMq7Z/GQ0eltv6QNB0+Ju3anmTPc85Uama1F6VBAIHwCBCgh6etqSkCCCCAAAIIIBAnoME4AXkcCxsQQACBvAjwDHpe2DkpAggggAACCCCAAAIIIIAAAtECBOjRHrxDAAEEEEAAAQQQQAABBBBAIC8CdHHPCzsnRQABBBBAAAEE/BPQ58j31jXJoSPH7WnUKiva0W3dP15yQgABBHImQICeM2pOhAACCCCAAAII+Cdw4sQJWbtprz0S+7KqXVEjsZdaI7GfP7SnXD6hnwwbWMlI7P6xkxMCCCAQqAABeqC8ZI4AAggggAACCPgvsHFbncx+epXU7DqQMHOdNm3Jmh32T19rLvPbZ4wSnV6NJRwC9KgIRztTy+IUIEAvznalVggggAACCCBQpAKrN+yRB3+93O7OnkkVNYi/5+eL5Xszx8rI07tlcghpClCAHhUF2GgUGYEEAgToCVDYhAACCCCAAAIImCigd869BOdOHfTZdD3uoVsmcifdQSmiV3pUFFFjUpXQCzCKe+gvAQAQQAABBBBAoBAE9A6pdmvXYDubRY/7b+t4zSffi3bBrtnVIO/X7Ldf9T1LdgLao0J7SCR73CE2V6dHhR5XDAvXUjG0InWIFOAOeqQG6wgggAACCCCAgKECOiBcpkFYsip8aHV3r9pUK8MGVSZLEth2umD7TxvWHhVcS/5fS+RojgABujltQUkQQAABBBBAAIGkAs8v2ZJ0n5cdC9+qznmAThdsLy2UWVq/elT87LuTC2qUf66lzK4PUhWuAF3cC7ftKDkCCCCAAAIIhERAu/EurdrpS22Xrt0puexSHvYu2L40WoJM/OxREZm9yV3GuZYiW4r1YhXgDnqxtiz1QgABBBBAAIGiEdhb1xQ1z3lLKqZTsNXWH5L2Za1bkk1Gx4a1C3ZGOC1M5GePiqEDu4oG/AuXVMuyql1R11ppaYmcP7SnXD6hnwwbWJm3u+0mXUv6JYb+n9RxHcratJLKinY5+f/UwkuGwwtEgAC9QBqKYiKAAAIIIIBAeAWyHRgumVjT4WPJdvm2PaxdsH0DTJGRnz0q3lqzQ27+8d9l256DCc+oX+gssdLoT98eHeX2GaMSzgQQVNCq+X60v1H+c847LR4g0Ut3/tj6dO1UJpu217f4S4zYfAnuE152od5IgB7q5qfyCCCAAAIIIFAIAnqXzs+lXdvgPwL62QU7H4Pa+eUdREDmZ48KHdQ/WXAea+CMAP+9mWNl5Ond7BkBgrjznmoQuNgyZfo+kwESI8/71prMHymJ/BKjV9eT5TKrt0GHdq2lY/s2MrB3J+naqZ3xPRQydSRd8ALB/3YOvg6cAQEEEEAAAQQQKGoBvcumXY01EGjp0srKR+8GOksQAaTmrd2l/Vg0n0IL0CMDPT+7jDv5/uHlDX7QZpWH9ub4P0++LTOmni6L3tmaNLiPDFr1zvs3rh4u5Se3SdstPN0gcFkV+n8Oevql9da1NClhFnren/xuhWz76OOE+zPduKP2Y3liwbqo5Pp/7niS/7uxTsl6KERlyJuiFiBAL+rmpXIIIIAAAgggUAwC+ry4Pges3Yxbuowb1lP0DvqajR+1uLtusrJo0L/MGozOj2Xp2h32oHa5eGbej/KmCzCzDcjS5etH2TPN48ixZvnNwvWZJrenB7znF0ui0kc+296za3vZvL1BqjbX2tfkUSv/IJa1m/bJ9O/+Vcac1V2mjT9N3lqzS/Y1HJJmqxvBqvc+kpZ//ZW41MmC89jUsT0UYvfzPhwCBOjhaGdqiQACCCCAAAIFLqCDdPkRoI8YfIp88+FXk86pnm0AGclrd8H2KdrRG4+5GtQusg6Zrkf2QKjeUS+P/7VKDlt3mTNZMg3IdPTyB3+9POtnsDMpS67TRF5nuTy3dulfvm63/ZPL82Z6Lu2hoG390C0TEz7rn2k+pCtcAQL0wm07So4AAggggAACLRCIDKwKYSRmHUFbuwprUJft0q1zO6v7bVXGgV6mAWRseeoOHI7d1KL3+627nH26d7TzSNVuqfa1qAAxBztdzRONeh6TNO1bDch+8PhSuWhMH7ns/H4ypG+XqGP0znmxBedRFeRNnIBeE//99CrxMqhdXCZsKFgBAvSCbToKjgACCCCAAAJeBVIFVpFdblNNJ5WrIDC2biUlJfYI2vf8fHHGAXZkHm1OKpW6g4flyFFv3YedZ45v/8ooGWUNDJZJV3O/R4lvOnIsaZd8i0XO6t/VquoJebd6nzVw2ae11n3jh/XKaIqwVO26dXeD/PODvVJ34JAcPX5CtNv9zr2Nn56ohWvaS+CV5VvtHy2zdsGeMrq3vL5yu6y07p4f9thmLSwOhxsgkMmgdgYUkyIEIECAHgAqWSKAAAIIIICAeQLpnuGN7HIbO52Ul8C+tr5JNm2rlwONR9xRnCsr2vsCMqh3hegI2l7vqLZtXSqdOrSVPfubsiqHPnP8n3NW2APVZTIntt+jxD/41Dv2c8KJCq8B+Trr2eVEi+5zpgjT55wvn9A/6ejaiUbt7t6lvdW9vkmOWUF5rhbTu2DnyoHziCx4c2PBDZBIu7VcgAC95YbkgAACCCCAAAKGC3h9hjeya3dHa+Tp2VZ302RdyyMD+2Qjreuo6VdeMFCmTxpgBbmlWWk5d3hPtqZv+vY1I2XuC+8lHUE78gSnWd3ip03sL7/485rIzVmtR9Y11XRSz/z9/azyT3aQDuLV0mVnbWPc6Nrp8ty9z7+75OnOxX4EYgWWVe0uqAESY8vP++wECNCzc+MoBBBAAAEEECgQgWyf4dWu3f9hTSelXY4z7RauAWyiRQc506mX5jy/Xm790giZfG6fuGR76xrj7rynmj9ZyzV0gHbtFnl3y76oKdh0WicdrX3a+P4y4NRy++533AlbuCHRdFItzJLDEUAgRsDkARJjispbnwQI0H2CJBsEEEAAAQQQME9Au6br3W8NtrNZ/J7uSfN75PerREc5/+KUIVZQ3Wx1Y90s817fJPusID52STV/st5U1mmpdNHB384ZXGk/f92lvEzGnt3dfm75uSWbramk/JnuLLZsvEcAgeAFtlgzAzgDJAZ/Ns5gggABugmtQBkQQAABBBAocgGne7YGyrkcMX3tpr1Ju6bnk1zvpFdZZVuzcW/K55sznT9Zny1/2RpkzFn+tOgDZ5VXBBAoYIE3/7FNJo3sXcA1oOheBQjQvYqRHgEEEEAAAQME8hHwpjtn5EjXFR3LZPigrtao4Uck0VRUmY6Yno46XZmeX7IlXRZ5279qw0d5OzcnRgCBwhDYtL2+MApKKX0TIED3jZKMEEAAAQQQ8CaQLriMzS1yJPGla3cGMp1UqnMuq9oV9ZyzBtljz+wmOmnXaivY9NIdPHKwMe2ePdzqnm3NkiXaPfu8oT2i5oJ+c/VWefntGtlvTRHW2RqJ/OKxfaSivJ0d+Cfqvj1h+CfTar27+SN7BO/YOvEeAQQQKBSBo8cSj2tRKOWnnN4FCNC9m3EEAggggAACWQtEBtmJAl5nCquBp3aypnc6ZD87rV3CGz4+Ir/4yz9l6+6DCc+tzyM700n16d5B7vjKuaJTcjlL5HlTBfc6FdXm7Q32FGEHG4/KC8u2yPaPPnayiXrVIHvZut1R27J5o92zdQ5oZ3G6Z7duVWLPOe1s19ctckBWW/NRp1och1Rp2IcAAggUgkAP63cyS7gECNDD1d7UFgEEEEAgjwJe5uFuSTE1iL/r/74h/+uKs62Bw07xFNy35Lx+H3s0h3NP+1128kMAAQT8ELjCmiKRJVwCBOjham9qiwACCCCQJwGv83C3tJga3P5qflVLs+F4BBBAAIE8CkwaGT8lYx6Lw6lzIECAngNkToEAAgggULwCf3xpvbxkPR/98aGjcnJZa7n0vL7y5UvPdCus+xdaA5XttwZLY0EAAQQQQCBTga7lbTJNSroiEiBAL6LGpCoIIIAAAskFYgcau8QKpCPvTKQKtGP3TRjaVZ5buiPu+eiPDx2X3734vv2TvCTsQQABBBBAIL3AY7MuSp+IFEUnQIBedE1KhRBAAIHiFogNliPvWMfuu2BEd3ntn7vlo/2HolCcgcZ+/LtVUdudN6kCbd03b/F2JymvCCCAAAII+C6gg3y2bdvW93zJ0HwBAnTz24gSIoAAAgUpEBsspwqk0+377IS+ct39izzdsdZA+pnXPixIOwqNAAIIIBBegS4d28rs2y8IL0DIa06AHvILgOojgEDhCMx79X15cdmHcqDpqHRs11qmjjtNrpo8xK5AtsGwHpztsYmOyzaQTtYtPNWd7MJpOUqKAAIIIIBAZgJ655zgPDOrYk1VYs2Las2cyhIGgRUrVsi1117rVnXu3LkyevRo930xrOyta5RN2+rt+Xs7tm8jA3t3ksoK5o/0q20brUGw9tY1ufMyV1a0k/bWoFjOkihgcwbLyja41LxT5ZvtvkLJ94qJp8kNDyySQ0ebHWZeEUAAAQQQQCBggX69yuXM0zrbn3v0886E4T2ltLSVNehntSxZsyPu7Jp+X/0he1rL2J0dykrkhJRag4kej93lvu/WuUx+ceeFOe3WHobYwAUuoBUC9IAaq7m5WebNmyfz58+XDRs2SGNjo5xyyikyatQoueaaa2TMmDEBnTl5tib9J0wXSKcKBGOP7d+ro7y1dpfMe32T/YsxVqBrpzK58oKBMn3SAHnmlQ1JR1tOFegtXLzJvnPZ8PERKT+5jX3nctrEge6pUh2b7T7NPNtjUx3nNd9LxvaRswaeYv9BemvNTrfOzsqIgZ1kbXW9HCd+dEh4RQABBBBAAIGABc7s11l+eNP5Ur3zgCx/d7fUWjcQulqB9NizusuQvl3ss+vgoIve2Sq1DYeka3mZTDqnuzz7+hap2X0waen6du8gs2+bJG3aJB9BXT+n1lrBeNPhY9Ku7UminzWdGxb6OXXzjgZpOHhYyju0lQFW4B55syi2TFPG9IkasDRpwQLYYVJsEED1CjZLAvQAmu7AgQNy8803y/LlyxPmXlJSIjNnzpRZs2Yl3B/Uxnz/J9QvLRa8uTlpIN2lvK2cN7Sn1B04LG+v2yXNzdGdO/r1tL6ZbGiyvpk8GhQR+SKAAAIIIIAAAggkEKjo0Fq+cslgeW31TtlvfVbrbD0nfbEVXPao7PjJl/hrd0hkv9zSEpFxw3rKWf26yrtbaiXRl/wTzukl08b3lw931ckrb2+VOiuorbCCWp1lw7kRsuaDPbL4nzvcO9kTrWOGD+6WoITeNi1ft0NeW7ld9tY3SWWndnLhuafK2LN7ecukwFPnOzYocL7Ais8z6D7T6hMDt912mxucT5w4UWbMmCGVlZWyfv16efzxx2X79u3y1FNPSZcuXeTGG2/0uQRmZvfqyq3ysz/9Q44eS36bdV/DYfl/b21JWoEtOxuS7mMHAggggAACCCCAgMjJZa3kOzNGyDvv7XWD2lGDK+QnT69N+bhUWetS+c2/T7F6Je6Rl5fXuEH4JWP7ysXn9XNpL5s42F13VoYNqpRUd5U/Z/VkTLVfj//sxEFOdlGvGoz7EZBHZWq90WA8bAF5rAHvzRQgQPe5Xf72t7/J4sWL7Vyvvvpqeeihh9wzjBgxQi677DL7OfCNGzfKo48+KtOnT5cePXq4aYpx5ZlF78uc59cXY9WoEwIIIIAAAgiEWMC6SWw9Wxy/6N3mnpUd7HFxjiS4OXFSqxKr23M72VXbGHdwh/at5WBj8t6CTtful96ukZetn0R3nTXTMUN7R+X9zDn97Pc6JoweV994RDpZ4/Xo3WpnwFFNoMF4ZEBuH5TBP9rF2+nmnSh5uv2JjmEbAmEUIED3udX1zrguHTp0kLvvvjsu94qKCrn//vvtIP3w4cMyZ84cueuuu+LSFcsGvXNOcF4srUk9EEAAAQQQKD6BZEG2U1MNiC+yuj+/anWHdrp2R95VTvfM8dbdDbJmU63st7pSd7a6Ug8f2FX6dC+3s091Vzld127tAu50A3fKmsmrBuORAXkmx5AGAQRyJ0CA7qP11q1b5d1337VznDx5smgwnmjRkdP79+8v1dXV8sILLxRtgK7PnGu3dhYEEEAAAQQQQEAFdDCtqy4YJCOGVEpV9T43aD2zb7n8ct46Wb9lf1Io587xsqpdUQN/RQ6y9X7NvqQDhumAr6nuOKcLiD8z/tPBYSMLqQOARQ4CFrlP1zUYdwLy2H2p7ioH1bU7tgy8RwABswQI0H1sj5UrV7q5jRs3zl1PtDJ27Fg7QNfn0WtqaqRv376JkhX0Nh0QLtUz5wVdOQqPAAIIIIBAkQvoaNZTz+stf1+xw+1GfeGonvJW1Z6MAul6a1DXVKNZn9azU5Tgj2/9F/t9ukB50sjko17r6N3OCN5RmVtv0t1xJiCOFeM9AgjkQ4AA3Ud1fa7cWfr16+esJnzt06ePu/2DDz4oygB9vjXtGQsCCCCAAAII5E9Ag+zJVlCtQXayZ47TjWY9/YLToypw5eQz7PfpAunKitYp7yxHZRrxhkA5AoNVBBAInQABuo9NvmvXLje3Xr1ST9PQs2dPN23kce7GAl/R57F0fkgWBBBAAAEEEGiZgI7K/fGh40kz0a7fV/5Lf2sqql1Jp4z6wsVnJT0+29GsCaSTkrIDAQQQyFqAAD1ruvgD6+vr3Y0nn3yyu55opX379u5mnTe92JZN2z61KLa6UR8EEEAAgcIX6NmlrVwwqo+s2WgN3mXN6VzWppXsbzgkdVa37GyXbp3L5Bd3Xmg9A70n6hnpSed0l2df3yI1uw8mzVrvdN/y+aGy6oN9UlvXJF2tEb7HntU9qrt2ujvW48/5tHde0hOxAwEEEEDAaAECdB+b58iRI25uZWVl7nqilcj9kcclSluI2w5YU3ewIIAAAggg0BKB1tZUVMeaT8iJBPNY6cjbncvbyr6Gw3GnGGqNkq1zX1Vtro3bN+GcXjJtfH/RNCUlJXJtRIoT1omqrNG2Fy6pliVrdkTs+WTVOXZ/Q6PVZXyb1FoBfdfyMokcpExTJnpGesp5nwwwlq47+VkDu8ed19nAHWtHglcEEECgeAUI0H1s21atWrm56R/9VIt+CHCW0tJSZ7VoXjta82qyIIAAAgiEQyDZ6NoXjOwpS9butkfVjviz56KUWn8qx1h3iScM7ymvr9qeNOBNNTJ3qmmqUu1zCxGxon+7hw2qtH/SHfsvo7Ib3DXb7uQRxWQVAQQQQKCIBQjQfWzcyG7rhw4dkjZtkgepOge6s6RK56QptNeBvaNHZi208lNeBBBAoBgFylqXym/+fYq8umpn0ummUk1Fla6LdaI7x5PH9LcpUwXZmmDy6NOSkqcamTvVNFWp9iU92f/saMmx6fJmPwIIIIAAAskECNCTyWSxPfK586amJikvL0+aS2Njo7uvU6fiC2Z1PlCd65SB4txmZgUBBBDIWEDvSE8c3sPqRr3dnd7qkvP6ytRxp8mP5qxIe0f6jNMq5O/vbEs6aneq6aZS7WtJF+tUQXbGMCREAAEEEECgyAUI0H1s4FNPPdXNbefOndK9e/LnyHS/s6RK56QpxNcrLxgoTyxYV4hFp8wIIIBARgL6jPRVF5wmb6zeLQeajkjHdm3k4rF95MuXnuke/8eX1ssry7cm3J/ujvT0C4a4+Tgr3//f4+zVdHekvzDlk6mwnON4RQABBBBAAAHzBQjQfWyjwYMHu7nV1NTIiBEj3PexK1u3bnU3DRo0yF0vppXpkwbInOfXy9FjzcVULeqCAAJFJOA8Az3r+tHyl79/kDSQThVkK8d105KjaLAeGbBHpuSOdKQG6wgggAACCCBAgO7jNaABuQ4wowPArVixQqZPn5409+XLl9v7dD703r17J01XyDt08LtbvzRCHvn9qkKuBmVHAAFDBPRu9W/vmyLPLanJKpBOd8c5VSCdap8hPBQDAQQQQAABBIpAgADdx0bUYFuD9NWrV8uLL74od911l3To0CHuDBq8V1dX29unTp0at7+YNkw+t4/steZz1TvpLAggEA6BlgbS6e5WpwqWU+3jGehwXH/UEgEEEEAAgUIWIED3ufWuu+46O0Cvq6uT++67T/7rv/5LIqdRq6+vt7fraVu3bi1f/epXfS6Bedl9ccoQqaxoJz/70z/o7m5e81CiAhBoZc3EOPrMbvL2uj1xpXXmZV63eY8sWr4t4XPO6QLeVPuz3acFTRUsZ7svDoANCCCAAAIIIIBAEQmUWN2xP52Qu4gqls+qfO1rX5PFixfbRRg9erRcf/319oBxGzZskMcee0y2b99u77vjjjvk61//es6Kqnfur732Wvd8c+fOFS1frpbm5mb525vVMu/1jQlHd+9S3lbGDe1pj1i8rGqXNDd/emnqrPKn9SqXffWHpOHjI7kqMufJUMCZuulviz+M6no8ZUxvOXtgN1m4pFqWrNkRl1smwaUelG2QmOq4Qsw33bzMccBsQAABBBBAAAEEkgjkOzZIUqzQbyZAD+ASOHjwoNx0003yzjvvJM195syZMmvWLPuZ9aSJfN5h0n/CvXWNsnlHgzQcPCzlHdrKACv41qnZnCVVIJLq2FTz92reqQK2VPuCyjfVOVtS3qDynffq+/bcyfWNR6RT+zai0z5dNTl+lGmnHSNfU7VpZDrWEUAAAQQQQAABBIIXMCk2CL62hXMGAvSA2krvFs+fP18WLFgg7733nhw4cEA6d+4sI0eOtO9ijxv3yTQ5AZ0+Ybb8J0zIwkYEEEAAAQQQQAABBEInQGxgZpPzDHpA7aLPnV999dX2T0CnIFsEEEAAAQQQQAABBBBAAIEiErCGHmJBAAEEEEAAAQQQQAABBBBAAIF8CxCg57sFOD8CCCCAAAIIIIAAAggggAAClgABOpcBAggggAACCCCAAAIIIIAAAgYIEKAb0AgUAQEEEEAAAQQQQAABBBBAAAECdK4BBBBAAAEEEEAAAQQQQAABBAwQIEA3oBEoAgIIIIAAAggggAACCCCAAAIE6FwDCCCAAAIIIIAAAggggAACCBggQIBuQCNQBAQQQAABBBBAAAEEEEAAAQQI0LkGEEAAAQQQQAABBBBAAAEEEDBAgADdgEagCAgggAACCCCAAAIIIIAAAggQoHMNIIAAAggggAACCCCAAAIIIGCAAAG6AY1AERBAAAEEEEAAAQQQQAABBBAgQOcaQAABBBBAAAEEEEAAAQQQQMAAAQJ0AxqBIiCAAAIIIIAAAggggAACCCBAgM41gAACCCCAAAIIIIAAAggggIABAgToBjQCRUAAAQQQQAABBBBAAAEEEECAAJ1rAAEEEEAAAQQQQAABBBBAAAEDBAjQDWgEioAAAggggAACCCCAAAIIIIAAATrXAAIIIIAAAggggACVQ8cCAAAZ1UlEQVQCCCCAAAIGCBCgG9AIFAEBBBBAAAEEEEAAAQQQQACBkyAIj0BjY2NUZTds2BD1njcIIIAAAggggAACCCAQDoHYWCA2VgiHgnm1JEA3r00CK9HWrVuj8n7ggQei3vMGAQQQQAABBBBAAAEEwikQGyuEUyH/taaLe/7bgBIggAACCCCAAAIIIIAAAgggIAToXAQIIIAAAggggAACCCCAAAIIGCBAF3cDGiFXRbjooouiTtW3b19p165d1DbeIIAAAggggAACCCCAQPELNDU1SU1NjVvR2FjB3cFKTgVKTlhLTs/IyRBAAAEEEEAAAQQQQAABBBBAIE6ALu5xJGxAAAEEEEAAAQQQQAABBBBAIPcCBOi5N+eMCCCAAAIIIIAAAggggAACCMQJEKDHkbABAQQQQAABBBBAAAEEEEAAgdwLEKDn3pwzIoAAAggggAACCCCAAAIIIBAnQIAeR8IGBBBAAAEEEEAAAQQQQAABBHIvQICee3POiAACCCCAAAIIIIAAAggggECcAAF6HAkbEEAAAQQQQAABBBBAAAEEEMi9AAF67s05IwIIIIAAAggggAACCCCAAAJxAgTocSRsQAABBBBAAAEEEEAAAQQQQCD3AgTouTfnjAgggAACCCCAAAIIIIAAAgjECRCgx5GwAQEEEEAAAQQQQAABBBBAAIHcCxCg596cMyKAAAIIIIAAAggggAACCCAQJ0CAHkfCBgQQQAABBBBAAAEEEEAAAQRyL0CAnntzzogAAggggAACCCCAAAIIIIBAnAABehwJGxBAAAEEEEAAAQQQQAABBBDIvQABeu7NOSMCCCCAAAIIIIAAAggggAACcQIE6HEkbEAAAQQQQAABBBBAAAEEEEAg9wIE6Lk354wIIIAAAggggAACCCCAAAIIxAkQoMeRsAEBBBBAAAEEEEAAAQQQQACB3AsQoOfenDMigAACCCCAAAIIIIAAAgggECdAgB5HwgYEEEAAAQQQQAABBBBAAAEEci9AgJ57c86IAAIIIIAAAggggAACCCCAQJzASXFb2BAagebmZpk3b57Mnz9fNmzYII2NjXLKKafIqFGj5JprrpExY8aExiJMFb333nvlT3/6k9x0001y++23p6y6H9eIKXmkrCg70wrs3btXnn76aVm8eLFUV1fbvy86dOgggwcPlilTpsiXvvQlad++fdJ8TLkO/ChH0kqyI63A7t275be//a28/vrrsm3bNjt99+7dZeLEifLFL35RTj/99JR5+NF+puSRsqLszEqgoaFBpk2bJnv27JErrrhCHn744aT5mHId+FGOpJVkR1oB/V104403pk2nCfT31BNPPBGX1o82NCWPuMqxIS8CJSesJS9n5qR5FThw4IDcfPPNsnz58oTlKCkpkZkzZ8qsWbMS7mdjYQq8/PLL8s1vftMufLoA3Y9rxJQ8CrO1zCn1K6+8Yv8u0PZMtpx66qny85//XM4888y4JKZcB36UI65ybMhYQK+ju+++Ww4ePJjwmJNOOsn+4vDWW29NuN+P9jMlj4QVZGOLBe68805ZsGCBnU+qAN2U68CPcrQYLeQZPPbYY/LII49kpJAoQPejDU3JIyMEEuVEoNUPrCUnZ+IkxgjodzIanC9dutQuk/7C0Q9NN9xwgwwdOlQ++OAD0W+h//GPf0jbtm3l3HPPNabsFCR7Af2W+Fvf+pbot7S6jB49Ws4///yEGfpxjZiSR8IKsjFjAf0S7xvf+IY0NTVJ69atZcaMGfZ1dP3118ukSZPk0KFDsmXLFtEPGC+99JJ996pjx45u/qZcB36Uw60UK54FVq9eLV//+tft66VVq1Z2L61bbrnFfj3jjDPkvffeswN3vd5OPvlkGTlyZNQ5/Gg/U/KIqhhvfBPQL4Bmz57t5qe9MS699FL3vbNiynXgRzmcOvGavYD26Nm4caNUVlbK73//e/t3kvYiTfSj11OnTp3ck/nRhqbk4VaKFTMErAuDJWQCf/3rX08MGTLE/rHukMfVfv/+/Scuv/xye/+wYcNO7Ny5My4NGwpL4Kmnnjpx9tlnu+2u7W99Y5y0En5cI6bkkbSS7EgrYH2Z4/4u0Otn2bJlCY959NFH3WvrjjvuiEpjynXgRzmiKsYbTwJXXnmle41YPXnijrUeoTgxYcIEO82IESNO1NXVRaXxo/1MySOqYrzxRaC2tvbE+PHj3WtM/8Z95zvfSZi3KdeBH+VIWEE2ehK45JJL7OvmX//1Xz0dp4n9aENT8vBceQ4IVIBB4sz4niSnpbCCNft8+vyo3jmPXSoqKuT++++3Nx8+fFjmzJkTm4T3BSKgdza1K/tDDz0kR48eFb1zlcnixzViSh6Z1Jc0iQW0F43eWdBF7yacd955CRNqjxzrA7G9T++i63gWzmLKdeBHOZw68epNoKqqSt599137oKlTp8rFF18cl0HXrl3la1/7mr1dr5/XXnstKo0f7WdKHlEV440vAvqZRcfJ6NKlS9r8TLkO/ChH2sqSIKWAPm5TU1NjpznrrLNSpk200482NCWPRPVjW/4ECNDzZ5+XM2/dutX9oDR58mTRYDzRot2f+/fvb+964YUXEiVhm+ECc+fOlc9+9rPy6quv2iUdNGiQ+8VLqqL7cY2YkkeqerIvvcA777zjJtKB4JItOmaFdffT3n3kyBHZvHmzvW7KdeBHOZLVne3pBfSa0KC8b9++Yt2tSnrAgAED3H1Wzy133Y/2MyUPt1Ks+Cbw/PPPi35OKS0tle9///sp8zXlOvCjHCkrys6MBPTRGus2qJ020fgpqTLxow1NySNVPdmXHwEC9Py45+2sK1eudM89btw4dz3RytixY+3N27dvd79hTJSObWYKrF271r5r3qZNG/vZz2effdb+gJyutH5cI6bkka6u7E8tMHz4cLsHxlVXXeV+YZfsCOdDju7Xnje6mHId+FEOu0L8k5WAzgyiAwjqIJU6cFeyRf/WOEu3bt2cVa4jV4KVWAG9a/7AAw/Ym3Vg23POOSc2SdR7P34XmJJHVMV4k5WA07NHD7Ye4/KUhynXgR/l8FRxEudEgAA9J8zmnMTprqol6tevX8qC9enTx92vA8exFJaADvCn0xbpnQXruWB7wL9MauDHNWJKHpnUlzTJBfRLPJ2K70c/+pH06NEjeUJrz9tvv+3u1xHddTHlOvCjHG7lWAlEYN++ffLkk0/aeet0fdrDy1n8aD9T8nDqxKs/Avfdd59Y4+bYXyDedtttaTM15TrwoxxpK0uCtALr16+30+jApsePH7cfB9Rp+vTLaf1iUb+ctsZYsQdOjs3MjzY0JY/YuvE+/wLMg57/NshpCXbt2uWer1evXu56opWePXu6myOPczeyYrSAfnDRLn9el8i2zvYaMSUPr3UnfXYCOkOA80FHn0V3gnlTrgM/ypGdDEelEtCeFjoX+qJFi+yxTj766CPRxyXuvfde6dy5s3uoH+1nSh5upVhpscD8+fNFR27Xv3M6zop+KZ1uMeU68KMc6erK/vQCzh10HaNHHwnUV2fR30+6X39+97vfyc9+9jMZM2aMs1v8aENT8nArxYoxAgToxjRFbgpSX1/vnkinskm16F0MZ9EplFgKSyCb4Fxr6Mc1YkoehdVihVlavfOpXwY5izPQl7435TrwoxxO/Xj1R0AfwfnCF74QlZl+saMzv0bePdcEfrSfKXlEVZg3WQvs3r1bfvjDH9rHa9f22Gn5kmVsynXgRzmS1ZHtmQno2BibNm2yE+t0oXoXXa8lHQy1vLxcqqur5S9/+Yvo1I/aS0P/tuk0bDodsS5+tKEpedgV4h+jBAjQjWqO4Aujv5CcpayszFlN+Bq5P/K4hInZWDQCkW0deQ0kqmDk/sjjItcj0+Q6j0TnY5t/Ah9//LE9R7ozoJeOWzF9+nT3BKZcB36Uw60UK74I7NixIy4fvYP+xz/+UU455RT3Q7Am8qP9TMkjrtJsyEpAB4NraGiwH9X79re/nXEeplwHfpQj40qTMKGAPrrp3DHXRz6feOIJ6d27t5tWxzOwpoeUn/zkJ/KrX/3KHlvlrrvukueee87uteFHG5qSh1tpVowRIEA3pilyU5DIaba0K2GqJXLQp2zvxqbKn31mCvhxjZiSh5nCxVEq7VVz4403ik7Fpove/XzkkUeiHqsw5TrwoxzF0Wrm1EI/ED/22GP2tFh79uyRhQsXio7GrbNOLFu2zO5OOmnSJLvAfrSfKXmY0wKFW5JnnnlG3njjDbdre7ovgSNrasp14Ec5IuvFuneBM844wx64Uh+z0RkmIoPzyNx0DB+d0WT16tX2HXedAvKiiy6KmrY228/TflwHfuQRWV/WzRDw/oCqGeWmFFkKRHZb1y49qRZnJGZNoyOBs4RDwI9rxJQ8wtFiua+lBlTXXXedrFq1yj55ZWWlPcCX3vmMXEy5DvwoR2S9WG+5wOmnny4XXnihPRiTTsE2e/ZsefDBB+2Mm5qa5Lvf/a7oHMW6+NF+puRhV4h/shbQnhc6aKUuN9xwgz2Ql5fMTLkO/CiHl3qTNl5AA1sNzMePH580ONejNPj+8pe/7Gbw1ltv2et+tKEpebiVY8UYAQJ0Y5oiNwWJfO5cPwSlWhobG93dnTp1ctdZKW4BP64RU/Io7pbKT+103lidHcAZFE7vnM+ZM0cGDhwYVyBTrgM/yhFXOTb4LvD5z39epk6daudbV1cnL774or3uR/uZkofvaCHKUHv1/du//Zv9xY32wMhk1PZYHlOuAz/KEVs33gcnEDlHujMdpB9taEoewcmRc7YCBOjZyhXocc70R1p857nRZFWJ3N+9e/dkydheZAJ+XCOm5FFkTZP36uho7TNmzHBHrx0wYIA9aE6i4FwLa8p14Ec58o4fkgJceumlbk2dL4H8aD9T8nArx4pnAR2fwLl7ef3119uDeOk1EvnjDPqlmesz6s4+Hd9AF1OuAz/KYVeIf3IiEPkYhfPcuB9taEoeOUHkJJ4ECNA9cRV+4sGDB7uVqKmpcdcTrWzdutXdPGjQIHedleIW8OMaMSWP4m6p3NZu3rx5cvPNN4vTs0bniH366aejPvDGlsiU68CPcsTWjfeZC+h4BevWrbPviEeObZIoh4qKCnezM4CTH+1nSh5u5VjxLOCMd6EHPvDAA/YAXjqIV+SPjovhLPqForPvD3/4g73ZlOvAj3I49eQ1OwGdPu2ll16y/46l61FaW1vrnkQf6dLFjzY0JQ+3cqwYI0CAbkxT5KYgI0aMsJ+n0bOtWLEi5Ul1aglddD70ZINnpMyAnQUp4Mc1YkoeBdkABhb62WeflXvuuUeOHTtml+6yyy6T3/zmNxIZTCUqtinXgR/lSFQ/tmUmoMHU1VdfLd/61rdEH5FItUR+cayPT+jiR/uZkkequrMveAFTrgM/yhG8VnGf4fHHH5dbb73Vntox8sufRLVeuXKlu3n48OH2uh9taEoebuVYMUaAAN2YpshNQTTY1l8Iuujzfc4gPLFn1+Bd54DUxXkmMDYN74tTwI9rxJQ8irOFclsrHb1WpzRy7nx+9atftQf0ymTgSFOuAz/KkVv14jrbmDFj3Ar9+c9/dtdjV5qbmyVy/8SJE+0kfrSfKXnE1pn3mQvo4HAbNmxI+bNo0SI3wyuuuMJNq4GYLqZcB36Uw60oK1kJjBs3zj1u/vz57nrsit5dd3pgtG7dWpzHcPxoQ1PyiK0z7/MvQICe/zbIeQl09GVddBCe++67T/RDUeRSX19vb9dt+stIP5CzhEvAj2vElDzC1XL+1la/wLvzzjvl+PHjdsY6iNe9997r9sLJ5GymXAd+lCOT+pImXuDyyy+Xzp072zv0OeKlS5fGJdIvgHQUd+0Kr8uECRNk2LBhbjo/2s+UPNxKsZIXAVOuAz/KkRfAIjmp/l5yeoEtWLBAXnnllbia6WM2d999tzgDw33lK1+RyNlK/GhDU/KIqzwb8irQ6gfWktcScPKcCwwZMsSeu1i7Er7//vvy9ttvS4cOHey76frMln4g//DDD+1y6SipOt8jS3EI6B8ZfZZYl9GjR8v555+fsGJ+XCOm5JGwgmzMSODJJ5+054nVxPqhRH837N+/X/bu3ZvyR7/YcwbVMeU68KMcGaGRKE5Ae1v06dPH7rWlX/boh2Gdqk+/HNbn03Xe8/vvv98dtV27tv/yl7+0/y45mfnRfqbk4dSJV/8FdGA4nVVCF53Kz7nbGXkmU64DP8oRWS/WvQm0bdvWHkNFn0PXLwi1V6n+XtLfV/p37s0335Tvfe974jzuqV3bf/zjH9s3rpwz+dGGpuTh1IlXMwRKrIvyhBlFoRS5FNA7YzfddJNo99Vky8yZM2XWrFme7pYly4vtZgjolzE6+q0u2v6333570oL5cY2YkkfSSrIjpYDOUx05m0PKxBE7H3roIfuZY2eTKdeBH+Vw6sSrd4HnnnvO7oHhDDSYKIehQ4fKT3/604TjnvjRfqbkkajubGu5wLZt22TKlCl2RtrF/eGHH06YqSnXgR/lSFhBNmYsoN3b9V5lqoHi9HGb2bNnS3l5eVy+frShKXnEVY4NeRMgQM8bff5PrHcv9BeT3s3QgXv0ToZ2Qxw5cqRce+21Evl8Tv5LSwn8EPASoOv5/LhGTMnDD78w5bFv376kPSzSOcQG6JrelOvAj3Kkqz/7kwvs3r1b5s6dK2+88YZoLy6dskj/7ujdqWnTpslnPvMZKS1N/vSdH+1nSh7JldiTrUCmAbrmb8p14Ec5svXiuE8E9Ito/b20ePFi9/eSjtauv5c+97nPuV/6JPPyow1NySNZHdmeWwEC9Nx6czYEEEAAAQQQQAABBBBAAAEEEgok/5o6YXI2IoAAAggggAACCCCAAAIIIIBAEAIE6EGokicCCCCAAAIIIIAAAggggAACHgUI0D2CkRwBBBBAAAEEEEAAAQQQQACBIAQI0INQJU8EEEAAAQQQQAABBBBAAAEEPAoQoHsEIzkCCCCAAAIIIIAAAggggAACQQgQoAehSp4IIIAAAggggAACCCCAAAIIeBQgQPcIRnIEEEAAAQQQQAABBBBAAAEEghAgQA9ClTwRQAABBBBAAAEEEEAAAQQQ8ChAgO4RjOQIIIAAAggggAACCCCAAAIIBCFAgB6EKnkigAACCCCAAAIIIIAAAggg4FGAAN0jGMkRQAABBBBAAAEEEEAAAQQQCEKAAD0IVfJEAAEEEEAAAQQQQAABBBBAwKMAAbpHMJIjgAACCCCAAAIIIIAAAgggEIQAAXoQquSJAAIIIIAAAggggAACCCCAgEcBAnSPYCRHAAEEEEAAAQQQQAABBBBAIAgBAvQgVMkTAQQQQAABBBBAAAEEEEAAAY8CBOgewUiOAAIIIIAAAggggAACCCCAQBACBOhBqJInAggggAACCCCAAAIIIIAAAh4FCNA9gpEcAQQQQAABBBBAAAEEEEAAgSAECNCDUCVPBBBAAAEEEEAAAQQQQAABBDwKEKB7BCM5AggggAACCCCAAAIIIIAAAkEIEKAHoUqeCCCAAAIIIIAAAggggAACCHgUIED3CEZyBBBAAAEEEEAAAQQQQAABBIIQIEAPQpU8EUAAAQQQQAABBBBAAAEEEPAoQIDuEYzkCCCAAAIIIIAAAggggAACCAQhQIAehCp5IoAAAggggAACCCCAAAIIIOBRgADdIxjJEUAAAQQQQAABBBBAAAEEEAhCgAA9CFXyRAABBBBAAAEEEEAAAQQQQMCjAAG6RzCSI4AAAggggAACCCCAAAIIIBCEAAF6EKrkiQACCCCAAAIIIIAAAggggIBHAQJ0j2AkRwABBBBAAAEEEEAAAQQQQCAIAQL0IFTJEwEEEEAAAQQQQAABBBBAAAGPAgToHsFIjgACCCCAAAIIIIAAAggggEAQAgToQaiSJwIIIIAAAggggAACCCCAAAIeBQjQPYKRHAEEEEAAAQQQQAABBBBAAIEgBAjQg1AlTwQQQAABBBBAAAEEEEAAAQQ8ChCgewQjOQIIIIAAAggggAACCCCAAAJBCBCgB6FKnggggAACCCCAAAIIIIAAAgh4FCBA9whGcgQQQAABBBBAAAEEEEAAAQSCECBAD0KVPBFAAAEEEEAAAQQQQAABBBDwKECA7hGM5AgggAACCCCAAAIIIIAAAggEIUCAHoQqeSKAAAIIIIAAAggggAACCCDgUYAA3SMYyRFAAAEEEEAAAQQQQAABBBAIQoAAPQhV8kQAAQQQQAABBBBAAAEEEEDAowABukcwkiOAAAIIIIAAAggggAACCCAQhAABehCq5IkAAggggAACCCCAAAIIIICARwECdI9gJEcAAQQQQAABBBBAAAEEEEAgCAEC9CBUyRMBBBBAAAEEEEAAAQQQQAABjwIE6B7BSI4AAggggAACCCCAAAIIIIBAEAIE6EGokicCCCCAAAIIIIAAAggggAACHgUI0D2CkRwBBBBAAAEEEEAAAQQQQACBIAQI0INQJU8EEEAAAQQQQAABBBBAAAEEPAoQoHsEIzkCCCCAAAIIIIAAAggggAACQQgQoAehSp4IIIAAAggggAACCCCAAAIIeBQgQPcIRnIEEEAAAQQQQAABBBBAAAEEghAgQA9ClTwRQAABBBBAAAEEEEAAAQQQ8ChAgO4RjOQIIIAAAggggAACCCCAAAIIBCFAgB6EKnkigAACCCCAAAIIIIAAAggg4FGAAN0jGMkRQAABBBBAAAEEEEAAAQQQCEKAAD0IVfJEAAEEEEAAAQQQQAABBBBAwKMAAbpHMJIjgAACCCCAAAIIIIAAAgggEITA/wcGeure8qA2DwAAAABJRU5ErkJggg==\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "TypeError",
     "evalue": "func_y() missing 2 required positional arguments: 'e' and 'error'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-77-348ece0d987b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m#plt.plot(xnew, func(xnew, *popt), 'k-')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxnew\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5000\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: func_y() missing 2 required positional arguments: 'e' and 'error'"
     ]
    }
   ],
   "source": [
    "xnew = np.linspace(X1[0], X1[-1], 10000)\n",
    "x = xnew\n",
    "a = 6.42674202e+02\n",
    "e = 4.61083069e-12\n",
    "error = res_lsq\n",
    "fig, ax = plt.subplots(nrows = 1, \n",
    "                        ncols = 1,  \n",
    "                        sharex = False, figsize=(5, 5)) \n",
    "xnew = np.linspace(X1[0], X1[-1], 10000)\n",
    "plt.plot(X1, y1, 'bo')\n",
    "#plt.plot(xnew, func(xnew, *popt), 'k-')\n",
    "plt.plot(xnew, func_y(x, error), 'r-')\n",
    "plt.ylim(0, 5000 )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Robust Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = df3.loc[df3['Density']< 0.052195] ['Flow'].values\n",
    "y1 = df3.loc[df3['Density']< 0.052195] ['Travel_time'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X1**0.55687059"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "resrlm = sm.RLM(y1, X,M=sm.robust.norms.HuberT()).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[103.16957413   1.55549158]\n"
     ]
    }
   ],
   "source": [
    "print(resrlm.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x149c18400>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = np.linspace(X1[0], X1[-1], 10000)\n",
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(X1, y1, 'o',label=\"data\")\n",
    "#ax.plot(x1, y_true2, 'b-', label=\"True\")\n",
    "#prstd, iv_l, iv_u = wls_prediction_std(res)\n",
    "#ax.plot(x1, res.fittedvalues, 'r-', label=\"OLS\")\n",
    "#ax.plot(x1, iv_u, 'r--')\n",
    "#ax.plot(x1, iv_l, 'r--')\n",
    "ax.plot(X1, resrlm.fittedvalues, 'o', label=\"RLM\")\n",
    "ax.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5057    0.503614\n",
       "Name: Od_flow, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_od_flow**4*1.80124916e-11/1.37400574e+02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>episode</th>\n",
       "      <th>step</th>\n",
       "      <th>edge_id</th>\n",
       "      <th>Time_to_collision</th>\n",
       "      <th>Space_mean_speed</th>\n",
       "      <th>Density</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Od_flow</th>\n",
       "      <th>CollisionRisk</th>\n",
       "      <th>Travel_time</th>\n",
       "      <th>TTC_size</th>\n",
       "      <th>TTC_threshold</th>\n",
       "      <th>max_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>11.668834</td>\n",
       "      <td>15.007125</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>167.850335</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.010177</td>\n",
       "      <td>107.238392</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>11.583477</td>\n",
       "      <td>14.867394</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>166.287479</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.010059</td>\n",
       "      <td>108.246274</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>12.061462</td>\n",
       "      <td>14.857828</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>166.180491</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>108.315964</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>12.491839</td>\n",
       "      <td>14.792254</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>165.447063</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.010612</td>\n",
       "      <td>108.796129</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>106.387747</td>\n",
       "      <td>14.787090</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>165.389302</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>108.834125</td>\n",
       "      <td>safe</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18623</th>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.425873</td>\n",
       "      <td>0.644622</td>\n",
       "      <td>0.233015</td>\n",
       "      <td>540.743200</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.039012</td>\n",
       "      <td>2496.563987</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18640</th>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.613367</td>\n",
       "      <td>0.623272</td>\n",
       "      <td>0.233636</td>\n",
       "      <td>524.227629</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.037649</td>\n",
       "      <td>2582.084433</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18657</th>\n",
       "      <td>100</td>\n",
       "      <td>8</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.537197</td>\n",
       "      <td>0.627616</td>\n",
       "      <td>0.233636</td>\n",
       "      <td>527.881961</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.036957</td>\n",
       "      <td>2564.209613</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18674</th>\n",
       "      <td>100</td>\n",
       "      <td>9</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.546413</td>\n",
       "      <td>0.639856</td>\n",
       "      <td>0.233636</td>\n",
       "      <td>538.176924</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.031664</td>\n",
       "      <td>2515.158009</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18691</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>e3.37</td>\n",
       "      <td>2.066153</td>\n",
       "      <td>0.434953</td>\n",
       "      <td>0.234879</td>\n",
       "      <td>367.781025</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.034271</td>\n",
       "      <td>3700.027758</td>\n",
       "      <td>critical</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2735.902635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1043 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       episode  step edge_id  Time_to_collision  Space_mean_speed   Density  \\\n",
       "569          4     0   e3.37          11.668834         15.007125  0.003107   \n",
       "586          4     1   e3.37          11.583477         14.867394  0.003107   \n",
       "603          4     2   e3.37          12.061462         14.857828  0.003107   \n",
       "620          4     3   e3.37          12.491839         14.792254  0.003107   \n",
       "637          4     4   e3.37         106.387747         14.787090  0.003107   \n",
       "...        ...   ...     ...                ...               ...       ...   \n",
       "18623      100     6   e3.37           2.425873          0.644622  0.233015   \n",
       "18640      100     7   e3.37           2.613367          0.623272  0.233636   \n",
       "18657      100     8   e3.37           2.537197          0.627616  0.233636   \n",
       "18674      100     9   e3.37           2.546413          0.639856  0.233636   \n",
       "18691      100    10   e3.37           2.066153          0.434953  0.234879   \n",
       "\n",
       "             Flow  Od_flow  CollisionRisk  Travel_time  TTC_size  \\\n",
       "569    167.850335    200.0       0.010177   107.238392      safe   \n",
       "586    166.287479    200.0       0.010059   108.246274      safe   \n",
       "603    166.180491    200.0       0.009542   108.315964      safe   \n",
       "620    165.447063    200.0       0.010612   108.796129      safe   \n",
       "637    165.389302    200.0       0.008164   108.834125      safe   \n",
       "...           ...      ...            ...          ...       ...   \n",
       "18623  540.743200   5000.0       0.039012  2496.563987  critical   \n",
       "18640  524.227629   5000.0       0.037649  2582.084433  critical   \n",
       "18657  527.881961   5000.0       0.036957  2564.209613  critical   \n",
       "18674  538.176924   5000.0       0.031664  2515.158009  critical   \n",
       "18691  367.781025   5000.0       0.034271  3700.027758  critical   \n",
       "\n",
       "       TTC_threshold    max_value  \n",
       "569              3.5  2735.902635  \n",
       "586              3.5  2735.902635  \n",
       "603              3.5  2735.902635  \n",
       "620              3.5  2735.902635  \n",
       "637              3.5  2735.902635  \n",
       "...              ...          ...  \n",
       "18623            3.5  2735.902635  \n",
       "18640            3.5  2735.902635  \n",
       "18657            3.5  2735.902635  \n",
       "18674            3.5  2735.902635  \n",
       "18691            3.5  2735.902635  \n",
       "\n",
       "[1043 rows x 13 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.80864705  1.34289618]\n"
     ]
    }
   ],
   "source": [
    "# BPR function form parameter estimation t = t0(1+a(v/cap)**b)\n",
    "# calculate t0\n",
    "t0 = df3[\"Travel_time\"].min()\n",
    "cap = df3[\"Flow\"].max()\n",
    "density_opt = df3.Density[df3[\"Flow\"] == cap].mean()\n",
    "# BPR function reorganized: log(t-t0) - log(t0) = log(a) + b*log(v/cap)\n",
    "\n",
    "# flow = df3.loc[(df3['Od_flow']< cap) & (df3['Travel_time'] != t0)] ['Flow'].values\n",
    "# t = df3.loc[(df3['Od_flow']< cap) & (df3['Travel_time'] != t0)] ['Travel_time'].values\n",
    "\n",
    "flow = df3.loc[(df3['Density']< density_opt) & (df3['Travel_time'] != t0)] ['Flow'].values\n",
    "t = df3.loc[(df3['Density']< density_opt) & (df3['Travel_time'] != t0)] ['Travel_time'].values\n",
    "\n",
    "# create X1 and y1\n",
    "\n",
    "y1 = np.log((t - t0)/t0)\n",
    "X1 = np.log(flow/cap)\n",
    "\n",
    "X = sm.add_constant(X1)\n",
    "resrlm = sm.RLM(y1,X,M=sm.robust.norms.HuberT()).fit()\n",
    "\n",
    "print(resrlm.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.exp(resrlm.params[0])\n",
    "b = resrlm.params[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
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       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14a94d6d0>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = np.linspace(X1[0], X1[-1], 10000)\n",
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(np.exp(X1)*cap, (np.exp(y1)+1)*t0, 'o',label=\"data\")\n",
    "#ax.plot(x1, y_true2, 'b-', label=\"True\")\n",
    "#prstd, iv_l, iv_u = wls_prediction_std(res)\n",
    "#ax.plot(x1, res.fittedvalues, 'r-', label=\"OLS\")\n",
    "#ax.plot(x1, iv_u, 'r--')\n",
    "#ax.plot(x1, iv_l, 'r--')\n",
    "ax.plot(X1, resrlm.fittedvalues, 'o', label=\"RLM\")\n",
    "ax.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
